chore: archive unused files and move legacy code to can_delete
archive/ — unused reference files (no active code references): - LookupSearchTermCleanedDrugName.csv, condition_directorate_mapping.csv - na_directory_rows.csv (diagnostic output), ta-recommendations.xlsx - snomed_indication_mapping_query.sql (source for embedded SQL) - IMPROVEMENT_RECOMMENDATIONS.md, power query.pq archive/can_delete/ — legacy code and logs safe to remove: - dashboard_gui.py (replaced by Reflex app) - pathways_app_old.py.bak (old backup) - Ralph loop iteration logs (iterations 2-8)
This commit is contained in:
Andrew Charlwood
@@ -0,0 +1,859 @@
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# Patient Pathway Analysis - Improvement Recommendations
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This document outlines recommended improvements to modernize the Patient Pathway Analysis application, based on multi-domain expert analysis.
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---
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## Executive Summary
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| Area | Current State | Recommended Change | Priority |
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|------|--------------|-------------------|----------|
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| **GUI Framework** | CustomTkinter | **Reflex** (browser-based, native Plotly) | High |
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| **Data Storage** | CSV files (90MB+) | SQLite with caching | High |
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| **Data Source** | Manual CSV export | Direct Snowflake connection | Medium |
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| **Directory Assignment** | Multi-stage fallback | GP diagnosis codes as primary | Medium |
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| **Code Quality** | Monolithic, no types | Modular, typed, tested | Low |
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---
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## 1. GUI Framework: Replace CustomTkinter with Reflex or Flet
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### What
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Replace the CustomTkinter-based GUI with a modern Python framework. Two strong options:
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- **[Reflex](https://reflex.dev)** - React-based, runs in browser
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- **[Flet](https://flet.dev)** - Flutter-based, native desktop or browser
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### Why
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Since Python is approved and standalone `.exe` distribution isn't required, **both frameworks are viable**.
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| Criterion | CustomTkinter | Reflex | Flet |
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|-----------|---------------|--------|------|
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| UI paradigm | Native desktop | Browser (localhost) | Desktop or browser |
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| Component richness | Limited | 60+ React components | Material Design |
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| Styling | Manual/limited | Full CSS/Tailwind | Flutter theming |
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| Plotly integration | External HTML | **Native embed** | WebView needed |
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| State management | Manual | Automatic re-render | Manual updates |
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| Learning curve | Low | Moderate (React-like) | Low-moderate |
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| Community | Small | 22k+ GitHub stars | 12k+ GitHub stars |
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| Maturity | Stable | Active (v0.6+) | Active (v0.80+) |
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### Recommendation: **Reflex**
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Given that:
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1. Python is approved for users
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2. Standalone `.exe` not required
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3. **Interactive Plotly is required** (Reflex has native `rx.plotly()` component)
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Reflex is now the better choice because:
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- **Native Plotly support** - no need to open external browser windows
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- **Modern React-based UI** - cleaner, more customizable
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- **Simpler state management** - automatic re-rendering on state changes
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- **Better for data apps** - designed for dashboards and data visualization
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### How (Reflex)
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**Basic app structure:**
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```python
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import reflex as rx
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class State(rx.State):
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"""Application state."""
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start_date: str = "2019-04-01"
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end_date: str = "2025-04-30"
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selected_drugs: list[str] = []
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selected_trusts: list[str] = []
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analysis_running: bool = False
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chart_data: dict = {}
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async def run_analysis(self):
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self.analysis_running = True
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yield # Update UI
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# Run analysis (async)
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df = await self.load_and_process_data()
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self.chart_data = generate_plotly_figure(df)
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self.analysis_running = False
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def index() -> rx.Component:
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return rx.box(
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rx.hstack(
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# Sidebar with filters
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rx.vstack(
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rx.date_picker(
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value=State.start_date,
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on_change=State.set_start_date,
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),
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rx.checkbox_group(
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items=drug_list,
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value=State.selected_drugs,
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on_change=State.set_selected_drugs,
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),
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rx.button(
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"Run Analysis",
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on_click=State.run_analysis,
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loading=State.analysis_running,
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),
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width="300px",
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),
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# Main content - interactive Plotly chart
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rx.plotly(data=State.chart_data, layout=chart_layout),
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width="100%",
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)
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)
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app = rx.App()
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app.add_page(index)
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```
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**Key components mapping:**
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| Current Component | Reflex Equivalent |
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|-------------------|-------------------|
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| `CTkFrame` | `rx.box`, `rx.vstack`, `rx.hstack` |
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| `CTkButton` | `rx.button` |
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| `CTkCheckBox` | `rx.checkbox` |
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| `CTkSlider` | `rx.slider` |
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| `DateEntry` | `rx.date_picker` |
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| `CTkScrollableFrame` | `rx.scroll_area` |
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| `filedialog` | `rx.upload` |
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| Plotly HTML file | **`rx.plotly()`** - native embed! |
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**Running the app:**
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```bash
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# Install
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pip install reflex
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# Initialize (first time)
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reflex init
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# Run development server
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reflex run
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# Opens http://localhost:3000 in browser
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```
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**Background tasks with progress:**
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```python
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class State(rx.State):
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progress: int = 0
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status: str = ""
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async def run_analysis(self):
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self.status = "Loading data..."
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self.progress = 10
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yield
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df = load_data()
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self.status = "Processing..."
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self.progress = 50
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yield
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result = process_data(df)
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self.status = "Complete"
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self.progress = 100
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yield
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```
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### Alternative: Flet
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If you prefer a more desktop-like feel, Flet remains a good option:
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```python
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import flet as ft
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def main(page: ft.Page):
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page.title = "HCD Analysis"
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async def run_analysis(e):
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# Background task
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page.run_task(do_analysis)
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page.add(
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ft.Row([
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# Sidebar
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ft.Column([
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ft.DatePicker(),
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ft.ElevatedButton("Run", on_click=run_analysis),
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]),
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# Chart area (opens in browser for interactivity)
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ft.ElevatedButton("View Chart", on_click=open_chart),
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])
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)
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ft.app(target=main) # Desktop window
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# OR
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ft.app(target=main, view=ft.WEB_BROWSER) # Browser
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```
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### Effort Estimate
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- Learning Reflex basics: 2-3 days
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- Rewriting GUI: 1-2 weeks
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- Testing and polish: 3-5 days
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---
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## 2. Data Storage: SQLite Architecture
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### What
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Replace CSV-based data loading with a SQLite database that stores reference data in normalized tables and caches processed patient data.
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### Why
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| Aspect | Current (CSV) | SQLite |
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|--------|---------------|--------|
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| Startup time | 90MB+ file read + full processing | Load reference data once (< 1MB) |
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| Memory usage | Entire dataset in memory | Incremental queries |
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| Incremental updates | Full reprocess required | Only process new/changed records |
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| Query performance | Pandas groupby/merge | Indexed SQL with CTEs |
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| Data consistency | Multiple CSVs can drift | Single source of truth with FK constraints |
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| Caching | None | Materialized views |
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**Expected improvements:**
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- 60-80% faster startup
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- 50-70% memory reduction
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- 90%+ time savings on incremental updates
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### How
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**Recommended schema (simplified):**
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```sql
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-- Reference tables
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CREATE TABLE ref_drug_names (
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drug_name_raw TEXT PRIMARY KEY,
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drug_name_std TEXT NOT NULL
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);
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CREATE TABLE ref_organizations (
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org_code TEXT PRIMARY KEY,
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org_name TEXT NOT NULL
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);
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CREATE TABLE ref_directories (
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directory_id INTEGER PRIMARY KEY,
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directory_name TEXT UNIQUE NOT NULL
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);
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CREATE TABLE ref_drug_directory_map (
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drug_name_std TEXT,
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directory_id INTEGER,
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is_single_valid BOOLEAN DEFAULT FALSE,
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PRIMARY KEY (drug_name_std, directory_id)
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);
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-- Patient data (fact table)
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CREATE TABLE fact_interventions (
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intervention_id INTEGER PRIMARY KEY,
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upid TEXT NOT NULL,
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provider_code TEXT,
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drug_name_std TEXT NOT NULL,
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intervention_date DATE NOT NULL,
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price_actual REAL,
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directory_id INTEGER,
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directory_assignment_method TEXT,
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data_load_batch_id INTEGER
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);
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-- Critical indexes
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CREATE INDEX idx_upid ON fact_interventions(upid);
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CREATE INDEX idx_upid_drug ON fact_interventions(upid, drug_name_std);
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CREATE INDEX idx_intervention_date ON fact_interventions(intervention_date);
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-- Materialized view for patient summaries (cached aggregations)
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CREATE TABLE mv_patient_treatment_summary (
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upid TEXT PRIMARY KEY,
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first_seen DATE,
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last_seen DATE,
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total_cost REAL,
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drug_count INTEGER,
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last_refresh TIMESTAMP
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);
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-- File tracking for incremental updates
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CREATE TABLE processed_files (
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file_path TEXT PRIMARY KEY,
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file_hash TEXT NOT NULL,
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last_processed TIMESTAMP
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);
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```
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**Migration strategy:**
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1. **Phase 1**: Create schema, load reference tables from existing CSVs
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2. **Phase 2**: Develop incremental load scripts for patient data
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3. **Phase 3**: Build materialized views for aggregations
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4. **Phase 4**: Modify `dashboard_gui.py` to query SQLite instead of processing CSVs
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**Key query replacing pandas aggregation:**
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```sql
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-- Replaces ~200 lines of pandas groupby/merge
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WITH patient_drugs AS (
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SELECT
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upid,
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drug_name_std,
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MIN(intervention_date) as first_date,
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MAX(intervention_date) as last_date,
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COUNT(*) as intervention_count,
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SUM(price_actual) as drug_cost
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FROM fact_interventions
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WHERE intervention_date BETWEEN :start_date AND :end_date
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AND provider_code IN (:trust_filters)
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GROUP BY upid, drug_name_std
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)
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SELECT * FROM patient_drugs;
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```
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### Effort Estimate
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- Schema design and setup: 2-3 days
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- Migration scripts: 3-4 days
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- Query optimization: 2-3 days
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- Integration testing: 2-3 days
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---
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## 3. Snowflake Integration
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### What
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Enable direct download of HCD activity data from Snowflake servers, replacing manual CSV exports.
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### Why
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- Eliminates manual export step
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- Enables date-range filtering at query level (faster)
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- Automatic caching with TTL
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- Graceful fallback to local files if Snowflake unavailable
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### How
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**Authentication: SSO Browser Login**
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Using `externalbrowser` authenticator - opens system browser for SSO authentication:
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```python
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import snowflake.connector
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conn = snowflake.connector.connect(
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account="your_account.region",
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user="your.email@nhs.net",
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authenticator="externalbrowser",
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warehouse="ANALYTICS_WH",
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database="data_hub",
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schema="dwh"
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)
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```
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**Note**: User will see browser popup on first connection each session.
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**Configuration (`config/snowflake.toml`):**
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```toml
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[snowflake]
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account = "your_account.region"
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warehouse = "ANALYTICS_WH"
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database = "DataWarehouse"
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schema = "dwh"
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|
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[query]
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default_timeout = 300
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chunk_size = 100000
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[cache]
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enabled = true
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ttl_hours = 24
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directory = "./data/cache"
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```
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**Core connector pattern:**
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```python
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from snowflake.connector import connect
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class SnowflakeConnector:
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def fetch_activity_data(self, start_date, end_date, provider_codes=None):
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query = """
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SELECT
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"Provider Code",
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"PersonKey",
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"ProductDescription" as "Drug Name",
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"Intervention Date",
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"Price Actual",
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-- ... other columns
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FROM DataWarehouse.dwh.FactHighCostDrugs
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WHERE "Intervention Date" BETWEEN :start_date AND :end_date
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"""
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with self.connect() as conn:
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cursor = conn.cursor()
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cursor.execute(query, {'start_date': start_date, 'end_date': end_date})
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return cursor.fetch_pandas_all()
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```
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**Caching strategy:**
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| Scenario | Action |
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|----------|--------|
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| Same date range within 24 hours | Use cache |
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| Date range includes today | Query Snowflake (data may be updating) |
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| User clicks "Refresh" | Query Snowflake |
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| Snowflake unavailable | Fallback to local CSV/Parquet |
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**Data loader with fallback:**
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```python
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class DataLoader:
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def load_data(self, start_date, end_date, force_refresh=False):
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# 1. Try cache
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if self.cache and not force_refresh:
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cached = self.cache.get(start_date, end_date)
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if cached is not None:
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return cached, "cache"
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# 2. Try Snowflake
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try:
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df = self.snowflake.fetch_activity_data(start_date, end_date)
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self.cache.set(df, start_date, end_date)
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return df, "snowflake"
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except SnowflakeConnectionError:
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pass
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|
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# 3. Fallback to local files
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if self.fallback_file.exists():
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return pd.read_parquet(self.fallback_file), "local_file"
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raise RuntimeError("No data source available")
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```
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||||
|
||||
**Dependencies to add:**
|
||||
|
||||
```toml
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dependencies = [
|
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"snowflake-connector-python[pandas]>=3.12.0",
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||||
"cryptography>=42.0.0",
|
||||
]
|
||||
```
|
||||
|
||||
### Effort Estimate
|
||||
- Snowflake connector setup: 2-3 days
|
||||
- Caching layer: 1-2 days
|
||||
- GUI integration (data source selector): 1-2 days
|
||||
- Testing with real data: 2-3 days
|
||||
|
||||
---
|
||||
|
||||
## 4. GP Diagnosis Code Integration
|
||||
|
||||
### What
|
||||
Use GP diagnosis codes as the **primary source** for directory/specialty assignment, with existing logic as fallback.
|
||||
|
||||
### Why
|
||||
- More accurate: Diagnosis directly indicates specialty
|
||||
- Reduces "Undefined" assignments
|
||||
- Leverages existing NHS data linkage
|
||||
- Maintains current logic as safety net
|
||||
|
||||
### How
|
||||
|
||||
**NHS diagnosis code landscape:**
|
||||
|
||||
| Code System | Usage | Notes |
|
||||
|-------------|-------|-------|
|
||||
| **SNOMED CT** | GP systems (mandatory since 2018) | Primary source |
|
||||
| **ICD-10** | Secondary care | Maps FROM SNOMED CT |
|
||||
| **Read Codes** | Legacy only | Historical records |
|
||||
|
||||
**New priority chain:**
|
||||
|
||||
```
|
||||
1. Drug has single valid directory → use that (unchanged)
|
||||
2. [NEW] GP diagnosis available → map SNOMED/ICD-10 to directory
|
||||
3. Extract from clinical data fields (existing)
|
||||
4. Most frequent for same patient/drug (existing)
|
||||
5. UPID-based inference (existing)
|
||||
6. Default to "Undefined" (existing)
|
||||
```
|
||||
|
||||
**ICD-10 to Directory mapping (examples):**
|
||||
|
||||
```python
|
||||
ICD10_TO_DIRECTORY = {
|
||||
# Neoplasms (Chapter II)
|
||||
"C": ["MEDICAL ONCOLOGY", "CLINICAL ONCOLOGY", "CLINICAL HAEMATOLOGY"],
|
||||
|
||||
# Blood diseases (Chapter III)
|
||||
"D5": ["CLINICAL HAEMATOLOGY"],
|
||||
"D6": ["CLINICAL HAEMATOLOGY"],
|
||||
|
||||
# Endocrine (Chapter IV)
|
||||
"E10": ["DIABETIC MEDICINE"], # Type 1 diabetes
|
||||
"E11": ["DIABETIC MEDICINE"], # Type 2 diabetes
|
||||
|
||||
# Eye (Chapter VII)
|
||||
"H0": ["OPHTHALMOLOGY"],
|
||||
"H1": ["OPHTHALMOLOGY"],
|
||||
"H2": ["OPHTHALMOLOGY"],
|
||||
"H3": ["OPHTHALMOLOGY"],
|
||||
|
||||
# Musculoskeletal (Chapter XIII)
|
||||
"M05": ["RHEUMATOLOGY"], # Rheumatoid arthritis
|
||||
"M06": ["RHEUMATOLOGY"],
|
||||
"M32": ["RHEUMATOLOGY"], # SLE
|
||||
|
||||
# Genitourinary (Chapter XIV)
|
||||
"N0": ["NEPHROLOGY"],
|
||||
"N1": ["NEPHROLOGY"],
|
||||
"N18": ["NEPHROLOGY"], # CKD
|
||||
}
|
||||
```
|
||||
|
||||
**Multi-diagnosis resolution:**
|
||||
|
||||
```python
|
||||
def resolve_directory_from_diagnoses(diagnoses, drug_valid_dirs):
|
||||
"""
|
||||
When patient has multiple diagnoses:
|
||||
1. Filter to diagnoses mapping to directories valid for this drug
|
||||
2. Oncology diagnoses take priority (ICD-10 chapter C)
|
||||
3. Use most recent active diagnosis
|
||||
4. Default to first alphabetically (deterministic)
|
||||
"""
|
||||
valid_matches = []
|
||||
|
||||
for dx in diagnoses:
|
||||
icd10_prefix = dx.icd10_code[:3]
|
||||
possible_dirs = ICD10_TO_DIRECTORY.get(icd10_prefix, [])
|
||||
matching = set(possible_dirs) & set(drug_valid_dirs)
|
||||
|
||||
if matching:
|
||||
valid_matches.append({
|
||||
'directories': matching,
|
||||
'is_oncology': dx.icd10_code.startswith('C'),
|
||||
'date': dx.diagnosis_date
|
||||
})
|
||||
|
||||
if not valid_matches:
|
||||
return None # Fall back to existing logic
|
||||
|
||||
# Oncology priority
|
||||
oncology = [m for m in valid_matches if m['is_oncology']]
|
||||
if oncology:
|
||||
return sorted(oncology[0]['directories'])[0]
|
||||
|
||||
# Most recent
|
||||
valid_matches.sort(key=lambda x: x['date'], reverse=True)
|
||||
return sorted(valid_matches[0]['directories'])[0]
|
||||
```
|
||||
|
||||
**Data source options:**
|
||||
|
||||
1. **Snowflake linked data** (recommended): Query `data_hub.dwh.DimClinicalCoding` joined via `PatientPseudo`
|
||||
2. **Local CSV cache**: Pre-extracted GP diagnosis data for offline use
|
||||
3. **Hybrid**: Cache with Snowflake refresh
|
||||
|
||||
**GP Diagnosis Query (confirm column names via Snowflake MCP):**
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
PatientPseudo,
|
||||
SNOMEDCode, -- or similar
|
||||
ICD10Code, -- may need mapping from SNOMED
|
||||
DiagnosisDate,
|
||||
DiagnosisStatus -- Active/Resolved if available
|
||||
FROM data_hub.dwh.DimClinicalCoding
|
||||
WHERE PatientPseudo IN (:patient_pseudo_list)
|
||||
ORDER BY DiagnosisDate DESC
|
||||
```
|
||||
|
||||
**New reference file needed (`./data/diagnosis_directory_map.csv`):**
|
||||
|
||||
```csv
|
||||
icd10_prefix,directory,priority,notes
|
||||
C,MEDICAL ONCOLOGY,1,All malignancies
|
||||
C81,CLINICAL HAEMATOLOGY,1,Hodgkin lymphoma
|
||||
C90,CLINICAL HAEMATOLOGY,1,Multiple myeloma
|
||||
E10,DIABETIC MEDICINE,1,Type 1 diabetes
|
||||
E11,DIABETIC MEDICINE,1,Type 2 diabetes
|
||||
G35,NEUROLOGY,1,Multiple sclerosis
|
||||
H0,OPHTHALMOLOGY,1,Eye disorders
|
||||
M05,RHEUMATOLOGY,1,Rheumatoid arthritis
|
||||
N18,NEPHROLOGY,1,Chronic kidney disease
|
||||
```
|
||||
|
||||
**Tracking assignment source (for audit):**
|
||||
|
||||
```python
|
||||
df['Directory_Source'] = pd.NA # New column
|
||||
|
||||
# After each assignment step:
|
||||
df.loc[assigned_mask, 'Directory_Source'] = 'DRUG_SINGLE' # Step 1
|
||||
df.loc[assigned_mask, 'Directory_Source'] = 'GP_DIAGNOSIS' # Step 2 (NEW)
|
||||
df.loc[assigned_mask, 'Directory_Source'] = 'CLINICAL_EXTRACT' # Step 3
|
||||
# ... etc
|
||||
```
|
||||
|
||||
### Prerequisites
|
||||
- Explore `data_hub.dwh.DimClinicalCoding` schema to confirm exact column names (use Snowflake MCP)
|
||||
- Map `PatientPseudo` to your HCD data (may need to add PatientPseudo to your data extract)
|
||||
- Obtain SNOMED CT to ICD-10 mapping table from NHS TRUD (if DimClinicalCoding only has SNOMED)
|
||||
|
||||
### Effort Estimate
|
||||
- Mapping table creation: 2-3 days
|
||||
- Snowflake GP query development: 2-3 days
|
||||
- Integration with existing logic: 2-3 days
|
||||
- Validation and testing: 3-5 days
|
||||
|
||||
---
|
||||
|
||||
## 5. Code Quality Improvements
|
||||
|
||||
### What
|
||||
Modernize the codebase with better structure, type hints, error handling, and testing.
|
||||
|
||||
### Why
|
||||
- `generate_graph()` is 267 lines with complexity >30
|
||||
- Zero type hints across entire codebase
|
||||
- Global variables create hidden state
|
||||
- No automated tests
|
||||
- Print statements instead of logging
|
||||
|
||||
### How
|
||||
|
||||
**Quick wins (implement first):**
|
||||
|
||||
1. **Replace global variables** with dataclass:
|
||||
```python
|
||||
@dataclass
|
||||
class AnalysisFilters:
|
||||
start_date: date
|
||||
end_date: date
|
||||
last_seen: date
|
||||
minimum_patients: int
|
||||
selected_trusts: list[str]
|
||||
selected_drugs: list[str]
|
||||
selected_directories: list[str]
|
||||
custom_title: str = ""
|
||||
|
||||
def validate(self) -> list[str]:
|
||||
errors = []
|
||||
if self.start_date >= self.end_date:
|
||||
errors.append("Start date must be before end date")
|
||||
return errors
|
||||
```
|
||||
|
||||
2. **Externalize configuration:**
|
||||
```python
|
||||
@dataclass
|
||||
class PathConfig:
|
||||
data_dir: Path = Path("./data")
|
||||
|
||||
@property
|
||||
def drug_names_file(self) -> Path:
|
||||
return self.data_dir / "include.csv"
|
||||
|
||||
@property
|
||||
def org_codes_file(self) -> Path:
|
||||
return self.data_dir / "org_codes.csv"
|
||||
|
||||
# ... etc for all 7 reference files
|
||||
|
||||
def validate(self) -> list[str]:
|
||||
"""Check all required files exist at startup."""
|
||||
errors = []
|
||||
for file_path in [self.drug_names_file, self.org_codes_file, ...]:
|
||||
if not file_path.exists():
|
||||
errors.append(f"Required file not found: {file_path}")
|
||||
return errors
|
||||
```
|
||||
|
||||
3. **Add logging:**
|
||||
```python
|
||||
import logging
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler("./logs/analysis.log"),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger("PatientPathway")
|
||||
|
||||
# Replace all print() with:
|
||||
logger.info("Starting analysis...")
|
||||
logger.error(f"Failed to load file: {e}")
|
||||
```
|
||||
|
||||
4. **Extract `generate_graph()` into smaller functions:**
|
||||
```python
|
||||
def generate_graph(df, filters: AnalysisFilters, config: PathConfig):
|
||||
df = prepare_data(df, filters) # ~50 lines
|
||||
stats = calculate_statistics(df) # ~80 lines
|
||||
hierarchy = build_hierarchy(df, stats) # ~60 lines
|
||||
chart_data = prepare_chart_data(hierarchy) # ~40 lines
|
||||
return render_icicle_chart(chart_data, filters.custom_title) # ~40 lines
|
||||
```
|
||||
|
||||
**Recommended project structure:**
|
||||
|
||||
```
|
||||
project/
|
||||
├── gui.py # Entry point only
|
||||
├── core/
|
||||
│ ├── config.py # PathConfig, AnalysisFilters
|
||||
│ ├── models.py # Data models
|
||||
│ └── exceptions.py # Custom exceptions
|
||||
├── data_processing/
|
||||
│ ├── loader.py # File/Snowflake loading
|
||||
│ ├── transformer.py # Data transformations
|
||||
│ └── validator.py # Data validation
|
||||
├── analysis/
|
||||
│ ├── pathway_analyzer.py # Patient pathway calculations
|
||||
│ └── statistics.py # Statistical calculations
|
||||
├── visualization/
|
||||
│ └── plotly_generator.py # Graph generation
|
||||
└── tests/
|
||||
├── test_data_processing.py
|
||||
├── test_analysis.py
|
||||
└── test_config.py
|
||||
```
|
||||
|
||||
**Add development dependencies:**
|
||||
|
||||
```toml
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"pytest>=8.0.0",
|
||||
"pytest-cov>=4.1.0",
|
||||
"mypy>=1.8.0",
|
||||
"black>=24.0.0",
|
||||
"ruff>=0.2.0",
|
||||
]
|
||||
```
|
||||
|
||||
**Priority tests to write:**
|
||||
|
||||
```python
|
||||
# tests/test_data_processing.py
|
||||
def test_drop_duplicate_treatments_ascending():
|
||||
"""Verify first intervention kept when ascending=True."""
|
||||
# ...
|
||||
|
||||
def test_drop_duplicate_treatments_descending():
|
||||
"""Verify last intervention kept when ascending=False."""
|
||||
# ...
|
||||
|
||||
# tests/test_config.py
|
||||
def test_path_config_validates_missing_files():
|
||||
"""Verify validation catches missing reference files."""
|
||||
# ...
|
||||
|
||||
def test_analysis_filters_validates_date_range():
|
||||
"""Verify start date must be before end date."""
|
||||
# ...
|
||||
```
|
||||
|
||||
### Effort Estimate
|
||||
- Dataclasses and config: 1-2 days
|
||||
- Logging setup: 0.5 days
|
||||
- Extract `generate_graph()`: 2-3 days
|
||||
- Add type hints (public API): 1-2 days
|
||||
- Basic test coverage: 2-3 days
|
||||
|
||||
---
|
||||
|
||||
## Implementation Roadmap
|
||||
|
||||
### Phase 1: Foundation (2-3 weeks)
|
||||
1. Create `PathConfig` and `AnalysisFilters` dataclasses
|
||||
2. Set up logging infrastructure
|
||||
3. Design and create SQLite schema
|
||||
4. Migrate reference data CSVs to SQLite
|
||||
|
||||
### Phase 2: Data Layer (2-3 weeks)
|
||||
1. Implement Snowflake connector with SSO browser auth
|
||||
2. Build caching layer with TTL
|
||||
3. Create data loader with fallback chain
|
||||
4. Migrate `dashboard_gui.py` to use SQLite queries
|
||||
|
||||
### Phase 3: Diagnosis Integration (2-3 weeks)
|
||||
1. Explore `data_hub.dwh.DimClinicalCoding` schema via Snowflake MCP
|
||||
2. Create ICD-10 to directory mapping table
|
||||
3. Implement GP diagnosis lookup using `PatientPseudo` linkage
|
||||
4. Integrate into `department_identification()` as step 2
|
||||
5. Add `Directory_Source` tracking column
|
||||
|
||||
### Phase 4: GUI Modernization (3-4 weeks)
|
||||
1. Learn Reflex fundamentals
|
||||
2. Recreate main window and navigation with `rx.vstack`/`rx.hstack`
|
||||
3. Implement filter panels (date pickers, checkbox groups)
|
||||
4. Integrate Plotly charts with native `rx.plotly()` component
|
||||
5. Test with `reflex run`
|
||||
|
||||
### Phase 5: Quality & Polish (1-2 weeks)
|
||||
1. Add type hints to public API
|
||||
2. Write priority unit tests
|
||||
3. Extract `generate_graph()` into smaller functions
|
||||
4. Documentation and cleanup
|
||||
|
||||
---
|
||||
|
||||
## Configuration Decisions
|
||||
|
||||
Based on requirements, the following decisions have been made:
|
||||
|
||||
| Question | Decision |
|
||||
|----------|----------|
|
||||
| **Snowflake auth** | SSO browser login (`authenticator='externalbrowser'`) |
|
||||
| **GP diagnosis data** | `data_hub.dwh.DimClinicalCoding` |
|
||||
| **Patient linkage** | Use `PatientPseudo` (anonymized identifier) - NOT UPID |
|
||||
| **Plotly interactivity** | Must be interactive - **Reflex has native `rx.plotly()` component** |
|
||||
| **Distribution** | Python script (`reflex run`) - no .exe needed |
|
||||
|
||||
### Implications
|
||||
|
||||
**Snowflake SSO**: Connection code becomes:
|
||||
```python
|
||||
conn = snowflake.connector.connect(
|
||||
account="your_account.region",
|
||||
user=os.environ.get("SNOWFLAKE_USER"),
|
||||
authenticator="externalbrowser", # Opens browser for SSO
|
||||
warehouse="ANALYTICS_WH",
|
||||
database="data_hub",
|
||||
schema="dwh"
|
||||
)
|
||||
```
|
||||
|
||||
**Patient Linkage**: The GP diagnosis query needs to join on `PatientPseudo`, not UPID:
|
||||
```sql
|
||||
SELECT
|
||||
cc.PatientPseudo,
|
||||
cc.SNOMEDCode, -- Confirm actual column names
|
||||
cc.ICD10Code,
|
||||
cc.DiagnosisDate
|
||||
FROM data_hub.dwh.DimClinicalCoding cc
|
||||
WHERE cc.PatientPseudo IN (:patient_list)
|
||||
```
|
||||
|
||||
**Note**: You'll need to confirm the exact column names in `DimClinicalCoding` - explore via Snowflake MCP or SQL client.
|
||||
|
||||
**Plotly Interactivity**: Reflex solves this elegantly with native embedding:
|
||||
```python
|
||||
# Interactive Plotly chart directly in the Reflex app
|
||||
rx.plotly(data=State.chart_data, layout=chart_layout)
|
||||
```
|
||||
Full interactivity (zoom, pan, hover tooltips) works in the browser-based app - no external HTML files needed.
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- [Reflex Documentation](https://reflex.dev/docs/)
|
||||
- [Reflex Plotly Component](https://reflex.dev/docs/library/graphing/plotly/)
|
||||
- [Flet Documentation](https://flet.dev/docs/) (alternative)
|
||||
- [Snowflake Python Connector](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector)
|
||||
- [NHS SNOMED CT](https://digital.nhs.uk/services/terminology-and-classifications/snomed-ct)
|
||||
- [NHS ICD-10 Classifications](https://isd.digital.nhs.uk/trud/users/guest/filters/0/categories/28)
|
||||
@@ -0,0 +1,165 @@
|
||||
,Search_Term,CleanedDrugName
|
||||
0,acute coronary syndrome,ABCIXIMAB|CLOPIDOGREL|PRASUGREL|RIVAROXABAN|TICAGRELOR
|
||||
1,acute lymphoblastic leukaemia,BLINATUMOMAB|DASATINIB|INOTUZUMAB|PEGASPARGASE|PONATINIB|TISAGENLECLEUCEL
|
||||
2,acute myeloid leukaemia,AZACITIDINE|DECITABINE|GEMTUZUMAB|GILTERITINIB|GLASDEGIB|LIPOSOMAL|MIDOSTAURIN|ORAL|VENETOCLAX
|
||||
3,acute promyelocytic leukaemia,ARSENIC|GEMTUZUMAB
|
||||
4,allergic asthma,OMALIZUMAB
|
||||
5,allergic rhinitis,SQ
|
||||
6,alzheimer's disease,DONEPEZIL
|
||||
7,amyloidosis,VUTRISIRAN
|
||||
8,anaemia,ERYTHROPOIESIS-STIMULATING|ERYTHROPOIETIN
|
||||
9,anaplastic large cell lymphoma,BRENTUXIMAB
|
||||
10,ankylosing spondylitis,ADALIMUMAB|GOLIMUMAB|SECUKINUMAB|UPADACITINIB
|
||||
11,apixaban,ANDEXANET
|
||||
12,aplastic anaemia,ELTROMBOPAG
|
||||
13,arthritis,ETANERCEPT
|
||||
14,asthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB
|
||||
15,atopic dermatitis,ABROCITINIB|ALCLOMETASONE|BARICITINIB|CRISABOROLE|DUPILUMAB|PIMECROLIMUS
|
||||
16,atrial fibrillation,APIXABAN|DABIGATRAN|DRONEDARONE|EDOXABAN|RIVAROXABAN|VERNAKALANT
|
||||
17,attention deficit hyperactivity disorder,ATOMOXETINE
|
||||
18,attention-deficit hyperactivity disorder,METHYLPHENIDATE
|
||||
19,axial spondyloarthritis,ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB|SECUKINUMAB|UPADACITINIB
|
||||
20,basal cell carcinoma,VISMODEGIB
|
||||
21,bipolar disorder,LOXAPINE|OLANZAPINE
|
||||
22,bladder,MIRABEGRON
|
||||
23,brca,OLAPARIB
|
||||
24,breast cancer,ABEMACICLIB|ALPELISIB|ANASTROZOLE|ATEZOLIZUMAB|BEVACIZUMAB|CAPECITABINE|DENOSUMAB|DOCETAXEL|ERIBULIN|EVEROLIMUS|FULVESTRANT|GEMCITABINE|INTRABEAM|LAPATINIB|NERATINIB|OLAPARIB|PACLITAXEL|PALBOCICLI|PALBOCICLIB|PEMBROLIZUMAB|PERTUZUMAB|RIBOCICLIB|SACITUZUMAB|TRASTUZUMAB|TUCATINIB|VINORELBINE
|
||||
25,cardiomyopathy,TAFAMIDIS
|
||||
26,cardiovascular disease,ATORVASTATIN
|
||||
27,cervical cancer,TOPOTECAN
|
||||
28,cholangiocarcinoma,PEMIGATINIB
|
||||
29,choroidal neovascularisation,AFLIBERCEPT|RANIBIZUMAB
|
||||
30,chronic kidney disease,DAPAGLIFLOZIN|IMLIFIDASE|ROXADUSTAT
|
||||
31,chronic liver disease,AVATROMBOPAG|LUSUTROMBOPAG
|
||||
32,chronic lymphocytic leukaemia,ACALABRUTINIB|BENDAMUSTINE|DUVELISIB|IBRUTINIB|IDELALISIB|OBINUTUZUMAB|OFATUMUMAB|RITUXIMAB|VENETOCLAX
|
||||
33,chronic myeloid leukaemia,ASCIMINIB|BOSUTINIB|STANDARD-DOSE|DASATINIB|DASITINIB|NILOTINIB|PONATINIB
|
||||
34,chronic obstructive pulmonary disease,ROFLUMILAST
|
||||
35,colon cancer,CAPECITABINE
|
||||
36,colorectal cancer,BEVACIZUMAB|CAPECITABINE|IRINOTECAN
|
||||
37,constipation,LUBIPROSTONE|METHYLNALTREXONE|NALDEMEDINE|NALOXEGOL|PRUCALOPRIDE
|
||||
38,covid-19,NIRMATRELVIR
|
||||
39,crohn's disease,INFLIXIMAB|VEDOLIZUMAB
|
||||
40,cutaneous t-cell lymphoma,BRENTUXIMAB|CHLORMETHINE
|
||||
41,cystic fibrosis,COLISTIMETHATE|LUMACAFTOR|MANNITOL
|
||||
42,cytomegalovirus,LETERMOVIR|MARIBAVIR
|
||||
43,deep vein thrombosis,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN
|
||||
44,depression,ESKETAMINE
|
||||
45,diabetes,ERTUGLIFLOZIN|INHALED|AFLIBERCEPT|BROLUCIZUMAB|DEXAMETHASONE|FARICIMAB|FLUOCINOLONE|RANIBIZUMAB
|
||||
46,diabetic retinopathy,RANIBUZIMAB
|
||||
47,diffuse large b-cell lymphoma,AXICABTAGENE|POLATUZUMAB|TISAGENLECLEUCEL
|
||||
48,dravet syndrome,CANNABIDIOL|FENFLURAMINE
|
||||
49,drug misuse,BUPRENORPHINE|NALTREXONE
|
||||
50,dry eye,CICLOSPORIN
|
||||
51,dyspepsia,LANSOPRAZOLE
|
||||
52,endometrial cancer,DOSTARLIMAB
|
||||
53,epilepsy,CENOBAMATE|GABAPENTIN|RETIGABINE
|
||||
54,fallopian tube,BEVACIZUMAB|NIRAPARIB|OLAPARIB|RUCAPARIB
|
||||
55,follicular lymphoma,DUVELISIB|IDELALISIB|LENALIDOMIDE|OBINUTUZUMAB|RITUXIMAB|TISAGENLECLEUCEL
|
||||
56,gastric cancer,CAPECITABINE|RAMUCIRUMAB|TRASTUZUMAB|TRIFLURIDINE
|
||||
57,gastro-oesophageal junction,NIVOLUMAB|PEMBROLIZUMAB
|
||||
58,giant cell arteritis,TOCILIZUMAB
|
||||
59,glioma,CARMUSTINE
|
||||
60,gout,CANAKINUMAB|FEBUXOSTAT|LESINURAD
|
||||
61,graft versus host disease,RUXOLITINIB
|
||||
62,granulomatosis with polyangiitis,AVACOPAN|MEPOLIZUMAB
|
||||
63,growth hormone deficiency,SOMATROPIN
|
||||
64,hand eczema,ALITRETINOIN
|
||||
65,heart failure,DAPAGLIFLOZIN|EMPAGLIFLOZIN|IVABRADINE|SACUBITRIL|VERICIGUAT
|
||||
66,hepatitis b,ADEFOVIR
|
||||
67,hepatitis c,BOCEPREVIR|DACLATASVIR|ELBASVIR|GLECAPREVIR|INTERFERON|LEDIPASVIR|OMBITASVIR|PEGINTERFERON|PEGYLATED|SIMEPREVIR|SOFOSBUVIR|TELAPREVIR
|
||||
68,hepatocellular carcinoma,ATEZOLIZUMAB|CABOZANTINIB|LENVATINIB|RAMUCIRUMAB|REGORAFENIB|SELECTIVE|SORAFENIB
|
||||
69,hiv,CABOTEGRAVIR
|
||||
70,hodgkin lymphoma,BRENTUXIMAB|NIVOLUMAB|PEMBROLIZUMAB
|
||||
71,hormone receptor,ABEMACICLIB
|
||||
72,hypercholesterolaemia,EZETIMIBE
|
||||
73,hyperparathyroidism,CINACALCET|ETELCALCETIDE
|
||||
74,immune thrombocytopenia,AVATROMBOPAG|FOSTAMATINIB
|
||||
75,influenza,AMANTADINE|ZANAMIVIR|BALOXAVIR
|
||||
76,insomnia,ZALEPLON
|
||||
77,irritable bowel syndrome,ELUXADOLINE
|
||||
78,ischaemic stroke,ALTEPLASE
|
||||
79,juvenile idiopathic arthritis,ABATECEPT|CANAKINUMAB|TOCILIZUMAB|TOFACITINIB
|
||||
80,kidney transplant,BASILIXIMAB
|
||||
81,leukaemia,FLUDARABINE|IMATINIB
|
||||
82,lung cancer,ATEZOLIZUMAB|DURVALUMAB|GEFITINIB|ORAL|NINTEDANIB
|
||||
83,lymphoma,BENDAMUSTINE|CRIZOTINIB|PIXANTRONE|RITUXIMAB
|
||||
84,macular degeneration,AFLIBERCEPT|BROLUCIZUMAB|FARICIMAB|RANIBIZUMAB
|
||||
85,macular oedema,AFLIBERCEPT|RANIBIZUMAB
|
||||
86,major depressive episodes,AGOMELATINE|VORTIOXETINE
|
||||
87,malignant melanoma,VEMURAFENIB
|
||||
88,malignant pleural mesothelioma,NIVOLUMAB|PEMETREXED
|
||||
89,manic episode,ARIPIPRAZOLE
|
||||
90,mantle cell lymphoma,AUTOLOGOUS|BORTEZOMIB|IBRUTINIB|LENALIDOMIDE|TEMSIROLIMUS
|
||||
91,melanoma,COBIMETINIB|DABRAFENIB|ENCORAFENIB|IPILIMUMAB|NIVOLUMAB|PEMBROLIZUMAB|TALIMOGENE|TRAMETINIB
|
||||
92,merkel cell carcinoma,AVELUMAB
|
||||
93,migraine,BOTULINUM|EPTINEZUMAB|ERENUMAB|FREMANEZUMAB|GALCANEZUMAB
|
||||
94,motor neurone disease,RILUZOLE
|
||||
95,multiple myeloma,BORTEZOMIB|THALIDOMIDE|CARFILZOMIB|DARATUMUMAB|DENOSUMAB|ELOTUZUMAB|ISATUXIMAB|IXAZOMIB|LENALIDOMIDE|PANOBINOSTAT|POMALIDOMIDE|SELINEXOR|TECLISTAMAB
|
||||
96,multiple sclerosis,ALEMTUZUMAB|BETA|CLADRIBINE|DACLIZUMAB|DIMETHYL|DIROXIMEL|FINGOLIMOD|INTERFERON|NATALIZUMAB|OCRELIZUMAB|OZANIMOD|PEGINTERFERON|PONESIMOD|SIPONIMOD|TERIFLUNOMIDE
|
||||
97,myelodysplastic,LENALIDOMIDE|LUSPATERCEPT
|
||||
98,myelofibrosis,FEDRATINIB|RUXOLITINIB
|
||||
99,myocardial infarction,ALTEPLASE|BIVALIRUDIN|TICAGRELOR
|
||||
100,myotonia,MEXILETINE
|
||||
101,narcolepsy,SOLRIAMFETOL
|
||||
102,neuroendocrine tumour,EVEROLIMUS|LUTETIUM
|
||||
103,non-small cell lung cancer,ATEZOLIZMAB|DOCETAXEL|ERLOTINIB|PEMETREXED
|
||||
104,non-small-cell lung cancer,AFATINIB|ALECTINIB|AMIVANTAMAB|ATEZOLIZUMAB|BEVACIZUMAB|BRIGATINIB|CEMIPLIMAB|CERITINIB|CRIZOTINIB|DABRAFENIB|DACOMITINIB|DURVALUMAB|ENTRECTINIB|ERLOTINIB|GEFITINIB|LORLATINIB|MOBOCERTINIB|NECITUMUMAB|NIVOLUMAB|OSIMERTINIB|PACLITAXEL|PEMBROLIZUMAB|PEMETREXED|PRALSETINIB|RAMUCIRUMAB|SELPERCATINIB|SOTORASIB|TEPOTINIB
|
||||
105,obesity,LIRAGLUTIDE|NALTREXONE|ORLISTAT|SEMAGLUTIDE|SIBUTRAMINE
|
||||
106,oesophageal cancer,NIVOLUMAB
|
||||
107,osteoarthritis,CELECOXIB
|
||||
108,osteoporosis,ALENDRONATE|DENOSUMAB|ORAL|ROMOSOZUMAB
|
||||
109,osteosarcoma,MIFAMURTIDE
|
||||
110,ovarian cancer,BEVACIZUMAB|PACLITAXEL|PEGYLATED|TOPOTECAN|TRABECTEDIN
|
||||
111,overweight,RIMONABANT
|
||||
112,pancreatic cancer,GEMCITABINE|OLAPARIB|PACLITAXEL|PEGYLATED
|
||||
113,paroxysmal nocturnal haemoglobinuria,PEGCETACOPLAN|RAVULIZUMAB
|
||||
114,peripheral arterial disease,NAFTIDROFYRYL
|
||||
115,plaque psoriasis,ADALIMUMAB|APREMILAST|BIMEKIZUMAB|BRODALUMAB|CERTOLIZUMAB|GUSELKUMAB|INFLIXIMAB|IXEKIZUMAB|RISANKIZUMAB|SECUKINUMAB|TILDRAKIZUAMB|USTEKINUMAB
|
||||
116,polycystic kidney disease,TOLVAPTAN
|
||||
117,polycythaemia vera,RUXOLITINIB
|
||||
118,pregnancy,ROUTINE
|
||||
119,primary biliary cholangitis,OBETICHOLIC
|
||||
120,primary hypercholesterolaemia,ALIROCUMAB|EVOLOCUMAB
|
||||
121,prostate cancer,ABIRATERONE|APALUTAMIDE|CABAZITAXEL|DAROLUTAMIDE|DEGARELIX|DENOSUMAB|DOCETAXEL|ENZALUTAMIDE|OLAPARIB|PADELIPORFIN|RADIUM-|RADIUM|SIPULEUCEL-T
|
||||
122,psoriasis,EFALUZIMAB
|
||||
123,psoriatic arthritis,ABATACEPT|ADALIMUMAB|APREMILAST|CERTOLIZUMAB|ETANERCEPT|GOLIMUMAB|GUSELKUMAB|IXEKIZUMAB|RISANKIZUMAB|TOFACITINIB|UPADACITINIB|USTEKINUMAB
|
||||
124,pulmonary embolism,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN
|
||||
125,pulmonary fibrosis,NINTEDANIB|PIRFENIDONE
|
||||
126,relapsing multiple sclerosis,OFATUMUMAB
|
||||
127,renal cell carcinoma,AVELUMAB|AXITINIB|BEVACIZUMAB|CABOZANTINIB|EVEROLIMUS|LENVATINIB|NIVOLUMAB|PAZOPANIB|PEMBROLIZUMAB|SUNITINIB|TIVOZANIB
|
||||
128,renal transplantation,BASILIXIMAB|INDUCTION
|
||||
129,retinal vein occlusion,AFLIBERCEPT|DEXAMETHASONE|RANIBIZUMAB
|
||||
130,rheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|CELECOXIB|CERTOLIZUMAB|ETANERCEPT|FILGOTINIB|GOLIMUMAB|RITUXIMAB|SARILUMAB|TOCILIZUMAB|TOFACITINIB|UPADACITINIB
|
||||
131,rivaroxaban,ANDEXANET
|
||||
132,schizophrenia,AMISULPRIDE|ARIPIPRAZOLE|LOXAPINE
|
||||
133,seizures,CANNABIDIOL
|
||||
134,sepsis,DROTRECOGIN
|
||||
135,severe persistent allergic asthma,OMALIZUMAB
|
||||
136,short bowel syndrome,TEDUGLUTIDE
|
||||
137,sickle cell disease,CRIZANLIZUMAB
|
||||
138,sleep apnoea,PITOLISANT|SOLRIAMFETOL
|
||||
139,smoking cessation,NICOTINE|VARENICLINE
|
||||
140,soft tissue sarcoma,INTRAVENOUS|NBTXR-|OLARATUMAB
|
||||
141,spinal muscular atrophy,NUSINERSEN|RISDIPLAM
|
||||
142,squamous cell,CETUXIMAB
|
||||
143,squamous cell carcinoma,CEMIPLIMAB|NIVOLUMAB|PEMBROLIZUMAB
|
||||
144,stem cell transplant,MELPHALAN|TREOSULFAN
|
||||
145,stroke,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN
|
||||
146,systemic lupus erythematosus,ANIFROLUMAB|ETANERCEPT
|
||||
147,systemic mastocytosis,MIDOSTAURIN
|
||||
148,thrombocytopenic purpura,ELTROMBOPAG|ROMIPLOSTIM
|
||||
149,thrombotic thrombocytopenic purpura,CAPLACIZUMAB
|
||||
150,thyroid cancer,CABOZANTINIB|LENVATINIB|SELPERCATINIB|VANDETANIB
|
||||
151,tophaceous gout,PEGLOTICASE
|
||||
152,transitional cell carcinoma,VINFLUNINE
|
||||
153,tuberous sclerosis,CANNABIDIOL
|
||||
154,type 1 diabetes,CONTINUOUS|DAPAGLIFLOZIN|INSULIN|SOTAGLIFLOZIN
|
||||
155,type 2 diabetes,CANAGLIFLOZIN|CONTINUOUS|DAPAGLIFLOZIN|EMPAGLIFLOZIN|ERTUGLIFLOZIN|EXENATIDE|FINERENONE|INSULIN|LIRAGLUTIDE|PIOGLITAZONE|ROSIGLITAZONE
|
||||
156,ulcerative colitis,ADALIMUMAB|INFLIXIMAB|FILGOTINIB|OZANIMOD|TOFACITINIB|UPADACITINIB|USTEKINUMAB|VEDOLIZUMAB
|
||||
157,urothelial carcinoma,ATEZOLIZUMAB|PEMBROLIZUMAB
|
||||
158,urticaria,OMALIZUMAB
|
||||
159,uterine fibroids,RELUGOLIX
|
||||
160,uveitis,ADALIMUMAB|FLUOCINOLONE
|
||||
161,vascular disease,MODIFIED-RELEASE|CLOPIDOGREL
|
||||
162,vasculitis,RITUXIMAB
|
||||
163,venous thromboembolism,APIXABAN|DABIGATRAN|RIVAROXABAN
|
||||
|
@@ -0,0 +1,647 @@
|
||||
import webbrowser
|
||||
from itertools import groupby
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
|
||||
from core import AnalysisFilters, PathConfig, default_paths
|
||||
from core.logging_config import get_logger
|
||||
from tools import data
|
||||
|
||||
# Import refactored analysis functions
|
||||
from analysis.pathway_analyzer import (
|
||||
generate_icicle_chart as _generate_icicle_chart,
|
||||
prepare_data as _prepare_data,
|
||||
calculate_statistics as _calculate_statistics,
|
||||
build_hierarchy as _build_hierarchy,
|
||||
prepare_chart_data as _prepare_chart_data,
|
||||
)
|
||||
|
||||
# Import visualization functions
|
||||
from visualization.plotly_generator import (
|
||||
create_icicle_figure as _create_icicle_figure,
|
||||
save_figure_html as _save_figure_html,
|
||||
figure_legacy as _figure_legacy,
|
||||
)
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
pd.options.mode.chained_assignment = None # default='warn'
|
||||
def human_format(num):
|
||||
num = float('{:.3g}'.format(num))
|
||||
magnitude = 0
|
||||
while abs(num) >= 1000:
|
||||
magnitude += 1
|
||||
num /= 1000.0
|
||||
return '{}{}'.format('{:f}'.format(num).rstrip('0').rstrip('.'), ['', 'K', 'M', 'B', 'T'][magnitude])
|
||||
|
||||
def main(dir, paths: Optional[PathConfig] = None):
|
||||
"""
|
||||
Load and process patient intervention data from a file.
|
||||
|
||||
Uses the FileDataLoader abstraction to handle CSV/Parquet file loading
|
||||
with all necessary transformations (patient_id, drug_names, department_identification).
|
||||
|
||||
Args:
|
||||
dir: Path to CSV or Parquet file
|
||||
paths: PathConfig for reference data locations (uses default_paths if None)
|
||||
|
||||
Returns:
|
||||
DataFrame with processed patient intervention data
|
||||
"""
|
||||
from data_processing.loader import FileDataLoader
|
||||
|
||||
if paths is None:
|
||||
paths = default_paths
|
||||
|
||||
loader = FileDataLoader(file_path=dir, paths=paths)
|
||||
result = loader.load()
|
||||
|
||||
logger.info("Initial data processing complete.")
|
||||
return result.df
|
||||
|
||||
|
||||
def drop_duplicate_treatments(df, ascending):
|
||||
df.sort_values(by=['Intervention Date'], ascending=ascending, inplace=True)
|
||||
df_treatment_steps = df.drop_duplicates(subset="UPIDTreatment", keep="first")
|
||||
if not ascending:
|
||||
df_treatment_steps.sort_values(by=['Intervention Date'], ascending=True, inplace=True)
|
||||
return df_treatment_steps
|
||||
|
||||
|
||||
def row_function(row):
|
||||
ids = ""
|
||||
parents = "N&WICS"
|
||||
count = row.count()
|
||||
for c in range(count):
|
||||
v = row[c]
|
||||
if type(v) != str:
|
||||
v = row[c + 1]
|
||||
if c == count - 1:
|
||||
ids = parents + " - " + v
|
||||
continue
|
||||
parents += " - " + v
|
||||
label = row[count - 1]
|
||||
value = parents + "," + label + "," + ids
|
||||
return value
|
||||
|
||||
|
||||
def count_list_values(x):
|
||||
return [len(list(group)) for key, group in groupby(sorted(x))]
|
||||
|
||||
|
||||
def sum_list_values(x):
|
||||
sum_list = []
|
||||
for count in range(len(x["Drug Name"])):
|
||||
if count == 0:
|
||||
sum_list.append(sum(x["Price Actual"][ : x["Drug Name"][count]]))
|
||||
else:
|
||||
sum_list.append(sum(x["Price Actual"][x["Drug Name"][count-1] : (x["Drug Name"][count-1] + x["Drug Name"][count])]))
|
||||
return sum_list
|
||||
|
||||
|
||||
def remove_nan_string(y):
|
||||
return [x for x in y if str(x) != 'nan']
|
||||
|
||||
|
||||
def min_max_treatment_dates(ice_df, row):
|
||||
ids = row[2]
|
||||
min_max = ice_df[ice_df["ids"].str.contains(ids)]
|
||||
min_date = str(min_max["First seen"].min().strftime('%Y-%m-%d'))
|
||||
max_date = str(min_max["Last seen"].max().strftime('%Y-%m-%d'))
|
||||
return min_date + ',' + max_date
|
||||
|
||||
|
||||
def start_date_drug(df, x):
|
||||
drug_count = x.notnull().sum()
|
||||
date_string = []
|
||||
for d in range(drug_count):
|
||||
UPID_date_var = str(x.name) + str(x[d])
|
||||
date = df.loc[UPID_date_var, "Intervention Date"]
|
||||
date_string.append(date)
|
||||
return date_string
|
||||
|
||||
|
||||
def end_date_drug(df, x):
|
||||
drug_count = x.notnull().sum()
|
||||
date_string = []
|
||||
# Need to -1 from drug count as start date gets counted from notnull above
|
||||
for d in range(drug_count - 1):
|
||||
UPID_date_var = str(x.name) + str(x[d])
|
||||
date = df.loc[UPID_date_var, "Intervention Date"]
|
||||
date_string.append(date)
|
||||
return date_string
|
||||
|
||||
|
||||
def list_to_string(x):
|
||||
list = x.ids.split(' - ')
|
||||
drug_list = list[len(list) - len(x.average_cost):]
|
||||
ret_string = ""
|
||||
for y in range(len(x.average_cost)):
|
||||
if (round(x.average_spacing[y], 0) > 1) and (round(x.average_administered[y], 0) > 2.5) and (int(x.value) > 0):
|
||||
string = "<br><b>" + str(drug_list[y]) + "</b><br>On average given " + str(
|
||||
round(x.average_administered[y], 1)) + \
|
||||
" times with a " + str(round(int(x.average_spacing[y]) / 7, 1)) + " weekly interval (" \
|
||||
+ str(round((int(x.average_spacing[y]) / 7) * round(x.average_administered[y], 1),
|
||||
0)) + " weeks total treatment length)"
|
||||
#"<br>Average annual cost per annum:" + \
|
||||
#str(human_format(
|
||||
# (x.cost / x.value) / (((int(x.average_spacing[y]) / 7) * round(x.average_administered[y], 1))/ 52)))
|
||||
else:
|
||||
string = "<br><b>" + str(drug_list[y]) + "</b><br>On average given " + str(
|
||||
round(x.average_administered[y], 1)) + \
|
||||
" times with a " + str(round(int(x.average_spacing[y]) / 7, 1)) + " weekly interval (" \
|
||||
+ str(round((int(x.average_spacing[y]) / 7) * round(x.average_administered[y], 1),
|
||||
0)) + " weeks total treatment length)"
|
||||
#"<br>Average annual cost per annum unavailable"
|
||||
|
||||
ret_string += string
|
||||
|
||||
return ret_string
|
||||
|
||||
|
||||
def drug_frequency_average(x):
|
||||
drug_count = x.index.str.contains("drug_").sum()
|
||||
freq = []
|
||||
for d in range(drug_count):
|
||||
if x["freq_" + str(d)] > 1:
|
||||
duration = ((x["end_date_" + str(d)] - x["start_date_" + str(d)]) / np.timedelta64(1, 'D'))
|
||||
if duration > 0:
|
||||
freq_calc = duration / (x["freq_" + str(d)] - 1)
|
||||
else:
|
||||
freq_calc = 0
|
||||
else:
|
||||
freq_calc = 0
|
||||
freq.append(freq_calc)
|
||||
return freq
|
||||
|
||||
|
||||
def cost_pp_pa(x):
|
||||
if x["avg_days"]/ np.timedelta64(1, 'D') > 0:
|
||||
return str(round(x["costpp"] / ((x["avg_days"] / np.timedelta64(1, 'D')) / 365), 2))
|
||||
else:
|
||||
return "N/A"
|
||||
|
||||
|
||||
def generate_graph(
|
||||
df1,
|
||||
start_date=None,
|
||||
end_date=None,
|
||||
last_seen=None,
|
||||
save_dir=None,
|
||||
trustFilter=None,
|
||||
drugFilter=None,
|
||||
directorateFilter=None,
|
||||
title=None,
|
||||
minimum_num_patients=None,
|
||||
*,
|
||||
filters: Optional[AnalysisFilters] = None,
|
||||
paths: Optional[PathConfig] = None,
|
||||
):
|
||||
"""
|
||||
Generate patient pathway icicle chart.
|
||||
|
||||
This function can be called in two ways:
|
||||
1. New style: Pass filters=AnalysisFilters(...) with all parameters encapsulated
|
||||
2. Legacy style: Pass individual parameters (start_date, end_date, etc.)
|
||||
|
||||
If both are provided, the filters object takes precedence.
|
||||
|
||||
Args:
|
||||
df1: DataFrame with processed patient data
|
||||
filters: AnalysisFilters object with all filter parameters (preferred)
|
||||
paths: PathConfig object for file paths (optional, uses default_paths if not provided)
|
||||
|
||||
Legacy parameters (used if filters is None):
|
||||
start_date, end_date, last_seen, save_dir, trustFilter, drugFilter,
|
||||
directorateFilter, title, minimum_num_patients
|
||||
"""
|
||||
# Use PathConfig for file paths
|
||||
if paths is None:
|
||||
paths = default_paths
|
||||
|
||||
# Extract parameters from AnalysisFilters if provided
|
||||
if filters is not None:
|
||||
start_date = filters.start_date
|
||||
end_date = filters.end_date
|
||||
last_seen = filters.last_seen_date
|
||||
save_dir = filters.output_dir
|
||||
trustFilter = filters.trusts
|
||||
drugFilter = filters.drugs
|
||||
directorateFilter = filters.directories
|
||||
title = filters.custom_title
|
||||
minimum_num_patients = filters.minimum_patients
|
||||
|
||||
df1["UPIDTreatment"] = df1["UPID"] + df1["Drug Name"]
|
||||
|
||||
# Get average number of doses count
|
||||
org_codes = pd.read_csv(paths.org_codes_csv, index_col=1)
|
||||
df1["Provider Code"] = df1["Provider Code"].map(org_codes["Name"])
|
||||
#df1.to_csv("./df1.csv", index=False)
|
||||
|
||||
df1 = df1[(df1["Provider Code"].isin(trustFilter)) & (df1["Drug Name"].isin(drugFilter)) & (df1["Directory"].isin(directorateFilter))]
|
||||
|
||||
if len(df1) == 0:
|
||||
logger.warning("No data found for selected filters.")
|
||||
return
|
||||
|
||||
# Find total cost for each patient - Total cost is ~£110Mil, about 30% is unattributable to a patient (no UPID)
|
||||
cost_df = df1[["UPID", "Price Actual"]]
|
||||
total_costs = pd.DataFrame(cost_df.groupby("UPID").sum())
|
||||
total_costs.rename(columns={"Price Actual": "Total cost"}, inplace=True)
|
||||
|
||||
# Series to map directory
|
||||
directory_df = df1[["UPID", "Directory"]]
|
||||
directory_df.drop_duplicates("UPID", inplace=True)
|
||||
directory_df.set_index("UPID", inplace=True)
|
||||
logger.info("Filtering unrelated interventions")
|
||||
|
||||
df_end_dates = drop_duplicate_treatments(df1, False)
|
||||
df1_unique = drop_duplicate_treatments(df1, True)
|
||||
logger.info("Identifying unique patients and interventions used")
|
||||
# Create list of total number of that drug for each patient
|
||||
df_drug_freq = df1.groupby("UPID").agg({"Drug Name": lambda x: list(x)}).reset_index().set_index("UPID")
|
||||
df_drug_cost = df1.groupby("UPID").agg({"Price Actual": lambda x: list(x)}).reset_index().set_index("UPID")
|
||||
df_drug_freq["Price Actual"] = df_drug_freq.index.map(df_drug_cost["Price Actual"])
|
||||
#df_drug_freq["Price Actual"] = df_drug_freq["Price Actual"].map(df_drug_cost)
|
||||
df_drug_freq["Drug Name"] = df_drug_freq["Drug Name"].apply(count_list_values)
|
||||
df_drug_freq["Drug cost total"] = df_drug_freq.apply(lambda x: sum_list_values(x), axis=1)
|
||||
|
||||
|
||||
# Aggregate interventions & dates of interventions into transposed list by UPID
|
||||
df_drugs = df1_unique.groupby("UPID").agg({"Drug Name": lambda x: list(x)}).reset_index().set_index("UPID")
|
||||
df_dates = df1_unique.groupby("UPID").agg({"Intervention Date": lambda x: list(x)}).reset_index().set_index("UPID")
|
||||
df_end_dates = df_end_dates.groupby("UPID").agg({"Intervention Date": lambda x: list(x)}).reset_index().set_index("UPID")
|
||||
|
||||
logger.info("Calculating each unique patient's intervention average frequency, cost and duration of each intervention")
|
||||
# The following sh*t show is to unwrap the lists into columns for different drugs, start/end dates, and average
|
||||
# frequency/average total injections of each one
|
||||
df_dates_unwrapped = pd.DataFrame(df_dates["Intervention Date"].values.tolist(), index=df_dates.index).add_prefix(
|
||||
'date_')
|
||||
df_end_dates_unwrapped = pd.DataFrame(df_end_dates["Intervention Date"].values.tolist(), index=df_end_dates.index).add_prefix(
|
||||
'date_end_')
|
||||
df_drugs_unwrapped = pd.DataFrame(df_drugs["Drug Name"].values.tolist(), index=df_drugs.index).add_prefix('drug_')
|
||||
|
||||
df_freq_unwrapped = pd.DataFrame(df_drug_freq["Drug Name"].values.tolist(), index=df_drug_freq.index).add_prefix(
|
||||
'freq_')
|
||||
start_dates = df1[["UPIDTreatment", "Intervention Date"]].sort_values(by=["Intervention Date"], ascending=True,
|
||||
inplace=False,
|
||||
ignore_index=True).drop_duplicates(
|
||||
subset="UPIDTreatment").set_index("UPIDTreatment")
|
||||
end_dates = df1[["UPIDTreatment", "Intervention Date"]].sort_values(by=["Intervention Date"], ascending=False,
|
||||
inplace=False,
|
||||
ignore_index=True).drop_duplicates(
|
||||
subset="UPIDTreatment").set_index("UPIDTreatment")
|
||||
|
||||
|
||||
|
||||
df_drugs_unwrapped["start_dates"] = df_drugs_unwrapped.apply(lambda x: start_date_drug(start_dates, x), axis=1)
|
||||
|
||||
df_ddrugs_unwrapped = pd.DataFrame(df_drugs_unwrapped["start_dates"].values.tolist(),
|
||||
index=df_drugs_unwrapped.index).add_prefix(
|
||||
'start_date_')
|
||||
df_drugs_unwrapped.drop(["start_dates"], inplace=True, axis=1)
|
||||
df_drugs_unwrapped["end_dates"] = df_drugs_unwrapped.apply(lambda x: start_date_drug(end_dates, x), axis=1)
|
||||
df_dddrugs_unwrapped = pd.DataFrame(df_drugs_unwrapped["end_dates"].values.tolist(),
|
||||
index=df_drugs_unwrapped.index).add_prefix(
|
||||
'end_date_')
|
||||
|
||||
df_drugs_unwrapped.drop(["end_dates"], inplace=True, axis=1)
|
||||
df_drugs_unwrapped = pd.merge(df_drugs_unwrapped, df_ddrugs_unwrapped, left_index=True, right_index=True)
|
||||
df_drugs_unwrapped = pd.merge(df_drugs_unwrapped, df_dddrugs_unwrapped, left_index=True, right_index=True)
|
||||
df_dddddrugs_unwrapped = pd.DataFrame(df_drug_freq["Drug Name"].values.tolist(),
|
||||
index=df_drugs_unwrapped.index).add_prefix(
|
||||
'freq_')
|
||||
df_drugs_unwrapped = pd.merge(df_drugs_unwrapped, df_dddddrugs_unwrapped, left_index=True, right_index=True)
|
||||
df_drugs_unwrapped["frequency"] = df_drugs_unwrapped.apply(lambda x: drug_frequency_average(x), axis=1)
|
||||
|
||||
df_ddddddrugs_unwrapped = pd.DataFrame(df_drugs_unwrapped["frequency"].values.tolist(),
|
||||
index=df_drugs_unwrapped.index).add_prefix(
|
||||
'spacing_')
|
||||
df_drugs_unwrapped = pd.merge(df_drugs_unwrapped, df_ddddddrugs_unwrapped, left_index=True, right_index=True)
|
||||
df_dddddddrugs_unwrapped = pd.DataFrame(df_drug_freq["Drug cost total"].values.tolist(),
|
||||
index=df_drugs_unwrapped.index).add_prefix('total_cost_drug_')
|
||||
df_drugs_unwrapped = pd.merge(df_drugs_unwrapped, df_dddddddrugs_unwrapped, left_index=True, right_index=True)
|
||||
df_drugs_unwrapped.drop(["frequency"], inplace=True, axis=1)
|
||||
|
||||
# Insert first & last date seen into df (need to add last date seen)
|
||||
df_drugs_unwrapped.insert(0, "First seen", df_dates_unwrapped.min(axis=1))
|
||||
df_drugs_unwrapped.insert(1, "Last seen", df_end_dates_unwrapped.max(axis=1))
|
||||
|
||||
# Merge info from activity data with grouped info, and total cost info
|
||||
patient_info = df1.drop_duplicates(subset="UPID", keep="first").set_index("UPID")
|
||||
patient_info = pd.merge(patient_info, df_drugs_unwrapped, left_index=True, right_index=True)
|
||||
patient_info = pd.merge(patient_info, df_freq_unwrapped, left_index=True, right_index=True)
|
||||
patient_info = pd.merge(patient_info, total_costs, left_index=True, right_index=True)
|
||||
|
||||
#patient_info.to_csv("patient_info.csv", index=False)
|
||||
|
||||
# Filter initiation based on years provided
|
||||
patient_info = patient_info[(patient_info['First seen'] >= str(start_date)) & (
|
||||
patient_info['First seen'] < str(end_date))]
|
||||
if title == "":
|
||||
title = "Patients initiated from " + str(start_date) + " to " + str(end_date)
|
||||
|
||||
# Filter last seen based on date provided
|
||||
patient_info = patient_info[patient_info['Last seen'] > str(last_seen)]
|
||||
|
||||
# Remove patients with 0 drug, by filling blanks with NaN & dropping rows
|
||||
patient_info.drug_0.replace('N/A', np.nan, inplace=True)
|
||||
patient_info.dropna(subset=['drug_0'], inplace=True)
|
||||
|
||||
# Calculate duation of treatment
|
||||
patient_info['Days treated'] = patient_info["Last seen"] - patient_info["First seen"]
|
||||
date_df = patient_info[["First seen", "Last seen", 'Days treated']]
|
||||
|
||||
# Create df for ice chart with hierarchy of plot
|
||||
number_of_drugs = np.count_nonzero(patient_info.columns.str.startswith('drug_'))
|
||||
final_drug_index = patient_info.columns.to_list().index("drug_" + str(number_of_drugs - 1))
|
||||
|
||||
upid_drugs_df = patient_info.iloc[:, (final_drug_index - number_of_drugs + 1):final_drug_index + 1]
|
||||
|
||||
upid_drugs_df.insert(0, "Trust", upid_drugs_df.index.str[:3])
|
||||
upid_drugs_df.insert(1, "Directory", upid_drugs_df.index)
|
||||
|
||||
upid_drugs_df["Trust"] = upid_drugs_df["Trust"].map(org_codes["Name"])
|
||||
upid_drugs_df["Directory"] = upid_drugs_df["Directory"].map(directory_df["Directory"])
|
||||
|
||||
l_df = pd.DataFrame()
|
||||
ice_df2 = pd.DataFrame()
|
||||
ice_df = pd.DataFrame()
|
||||
|
||||
upid_drugs_df["value"] = upid_drugs_df.apply(lambda x: row_function(x), axis=1)
|
||||
# Merge in date info
|
||||
upid_drugs_df = pd.merge(upid_drugs_df, date_df, left_index=True, right_index=True)
|
||||
|
||||
upid_drugs_df["ids"] = upid_drugs_df["value"].str.split(',').str[2]
|
||||
avg_treatment_dfs = pd.DataFrame(upid_drugs_df.groupby("ids", as_index=False)["Days treated"].mean()).set_index("ids")
|
||||
value_dfs = pd.DataFrame(upid_drugs_df.groupby("value", as_index=False).size()).reset_index()
|
||||
first_seen_treatment_dfs = pd.DataFrame(upid_drugs_df.groupby("ids", as_index=False)["First seen"].min()).set_index(
|
||||
"ids")
|
||||
last_seen_treatment_dfs = pd.DataFrame(upid_drugs_df.groupby("ids", as_index=False)["Last seen"].max()).set_index(
|
||||
"ids")
|
||||
|
||||
# Calculate total cost for parents
|
||||
upid_drugs_df["Cost"] = upid_drugs_df.index.map(total_costs["Total cost"])
|
||||
cost_dfs = pd.DataFrame(upid_drugs_df.groupby("value", as_index=False)['Cost'].sum()).set_index("value", drop=True)
|
||||
|
||||
# Calculate average dosing for each drug
|
||||
upid_drugs_df = pd.merge(upid_drugs_df, df_drugs_unwrapped, left_index=True, right_index=True)
|
||||
# frequency_dfs = pd.DataFrame(upid_drugs_df.groupby("value", as_index=False)['Cost'].sum()).set_index("value", drop=True)
|
||||
|
||||
# Calculate average spacing between drugs
|
||||
spacing_average = pd.DataFrame(upid_drugs_df.groupby("value", as_index=False)[
|
||||
[col for col in upid_drugs_df.columns if 'spacing_' in col]].mean()).set_index(
|
||||
"value", drop=True)
|
||||
spacing_average = spacing_average.round()
|
||||
spacing_average['combined'] = spacing_average.values.tolist()
|
||||
spacing_average["ids"] = spacing_average.index
|
||||
spacing_average["ids"] = spacing_average["ids"].str.split(',').str[2]
|
||||
spacing_average.set_index("ids", inplace=True)
|
||||
|
||||
# Calculate average cost for each drug
|
||||
cost_average = pd.DataFrame(upid_drugs_df.groupby("value", as_index=False)[
|
||||
[col for col in upid_drugs_df.columns if 'total_cost_drug_' in col]].mean()).set_index(
|
||||
"value", drop=True)
|
||||
cost_average = cost_average.round(2)
|
||||
cost_average['combined'] = cost_average.values.tolist()
|
||||
cost_average["ids"] = cost_average.index
|
||||
cost_average["ids"] = cost_average["ids"].str.split(',').str[2]
|
||||
cost_average.set_index("ids", inplace=True)
|
||||
|
||||
|
||||
# Calculate average number of doses
|
||||
freq_average = pd.DataFrame(upid_drugs_df.groupby("ids", as_index=False)[
|
||||
[col for col in upid_drugs_df.columns if 'freq_' in col]].mean()).set_index("ids",
|
||||
drop=True)
|
||||
# freq_average = freq_average.round()
|
||||
freq_average['combined'] = freq_average.values.tolist()
|
||||
|
||||
# Remove negative totals from "Cost" column
|
||||
num = cost_dfs._get_numeric_data()
|
||||
num[num < 0] = 0
|
||||
|
||||
value_dfs["Cost"] = value_dfs["value"].map(cost_dfs["Cost"])
|
||||
|
||||
ice_df[['parents', 'labels', 'ids']] = value_dfs["value"].str.split(',', expand=True)
|
||||
# ice_df["index"] = ice_df.ids
|
||||
# ice_df.set_index("index", inplace=True)
|
||||
|
||||
ice_df["average_administered"] = ice_df["ids"].map(freq_average["combined"])
|
||||
ice_df["cost"] = value_dfs["Cost"]
|
||||
ice_df["value"] = value_dfs["size"]
|
||||
|
||||
ice_df["average_cost"] = ice_df["ids"].map(cost_average["combined"])
|
||||
ice_df["average_cost"] = ice_df["average_cost"].apply(remove_nan_string)
|
||||
|
||||
ice_df["average_spacing"] = ice_df["ids"].map(spacing_average["combined"])
|
||||
ice_df["average_spacing"] = ice_df["average_spacing"].apply(remove_nan_string)
|
||||
ice_df["average_spacing"] = ice_df.apply(lambda x: list_to_string(x), axis=1)
|
||||
ice_df["average_spacing"] = ice_df["average_spacing"].str.replace("nan", "N/A")
|
||||
|
||||
|
||||
logger.info("Building graph dataframe structure.")
|
||||
# Add very top level of Trust
|
||||
new_row = pd.DataFrame({'parents': '', 'ids': "N&WICS", 'labels': 'N&WICS', 'value': 0, "cost": 0}, index=[0])
|
||||
ice_df = pd.concat(objs=[ice_df, new_row], ignore_index=True, axis=0)
|
||||
|
||||
# need to add parents as blocks...
|
||||
l3 = [x for x in ice_df.parents.unique() if x not in ice_df.ids]
|
||||
while len(l3) > 1:
|
||||
for l in l3:
|
||||
z = l.rfind("-")
|
||||
if z > 0:
|
||||
l_dict = {"parents": l[:z - 1], "ids": l, "value": 0, "labels": l[z + 2:], "cost": 0}
|
||||
l_df = pd.concat([l_df, pd.DataFrame(l_dict, index=[0])], ignore_index=True)
|
||||
ice_df2 = pd.concat([ice_df, l_df], ignore_index=True)
|
||||
l3 = [x for x in ice_df2.parents.unique() if x not in ice_df2.ids.unique()]
|
||||
ice_df = ice_df2.drop_duplicates("ids")
|
||||
|
||||
ice_df["level"] = ice_df["ids"].str.count('-')
|
||||
ice_df = ice_df[~ice_df['labels'].isin(["COST", "CHARGE", "N/A"])]
|
||||
ice_df.sort_values(by=["level"], ascending=False, inplace=True, ignore_index=True)
|
||||
|
||||
for index, row in ice_df.iterrows():
|
||||
lookup_index = ice_df.index[ice_df['ids'] == row['parents']]
|
||||
ice_df.loc[lookup_index, 'value'] = ice_df.loc[lookup_index, "value"] + ice_df.loc[index, "value"]
|
||||
ice_df.loc[lookup_index, 'cost'] = ice_df.loc[lookup_index, "cost"] + ice_df.loc[index, 'cost']
|
||||
|
||||
# Sum of parent values to create denominator for percentage - FOR PATIENT NUMBER COLOUR GRADING
|
||||
colour_df = pd.DataFrame(ice_df.groupby(["parents"])["value"].sum())
|
||||
ice_df['colour'] = ice_df["parents"].map(colour_df["value"])
|
||||
ice_df['colour'] = ice_df['value']/ice_df['colour']
|
||||
|
||||
# Sum of parent values to create denominator for percentage - FOR COST COLOUR GRADING
|
||||
#colour_df = pd.DataFrame(ice_df.groupby(["parents"])["cost"].sum())
|
||||
#ice_df['colour'] = ice_df["parents"].map(colour_df["cost"])
|
||||
#ice_df['colour'] = ice_df['cost'] / ice_df['colour']
|
||||
|
||||
|
||||
ice_df['costpp'] = ice_df['cost'] / ice_df['value']
|
||||
# Treatment length info
|
||||
ice_df['avg_days'] = ice_df["ids"].map(avg_treatment_dfs["Days treated"])
|
||||
ice_df['First seen'] = ice_df["ids"].map(first_seen_treatment_dfs["First seen"])
|
||||
ice_df['Last seen'] = ice_df["ids"].map(last_seen_treatment_dfs["Last seen"])
|
||||
|
||||
ice_df["dates"] = ice_df.apply(lambda x: min_max_treatment_dates(ice_df, x), axis=1)
|
||||
ice_df[['First seen (Parent)', 'Last seen (Parent)']] = ice_df["dates"].str.split(',', expand=True)
|
||||
|
||||
# Sort labels to be alphabetical
|
||||
# ice_df.sort_values(by=["labels"], ascending=True, inplace=True, ignore_index=True)
|
||||
ice_df['First seen'] = pd.to_datetime(ice_df['First seen'])
|
||||
ice_df['Last seen'] = pd.to_datetime(ice_df['Last seen'])
|
||||
ice_df["cost_pp_pa"] = ice_df.apply(lambda x: cost_pp_pa(x), axis=1)
|
||||
|
||||
# Filter out rows where value is less than minimum number of patients
|
||||
ice_df = ice_df[ice_df['value'] >= minimum_num_patients]
|
||||
|
||||
logger.info("Generating graph.")
|
||||
|
||||
figure(ice_df, title, save_dir)
|
||||
return
|
||||
|
||||
|
||||
def figure(ice_df4, dir_string, save_dir):
|
||||
"""
|
||||
Create and display icicle figure (legacy interface).
|
||||
|
||||
This function delegates to visualization.plotly_generator.figure_legacy()
|
||||
for backward compatibility.
|
||||
|
||||
Args:
|
||||
ice_df4: DataFrame with chart data
|
||||
dir_string: Title string (used for filename and chart title)
|
||||
save_dir: Directory to save the HTML file
|
||||
"""
|
||||
_figure_legacy(ice_df4, dir_string, save_dir)
|
||||
return
|
||||
|
||||
|
||||
# fig = go.Figure(go.Icicle(
|
||||
# labels=ice_df4.labels,
|
||||
# ids=ice_df4.ids,
|
||||
# # count="branches",
|
||||
# parents=ice_df4.parents,
|
||||
# customdata=np.stack((ice_df4.value, ice_df4.colour, ice_df4.cost, ice_df4.costpp, first_seen, last_seen,
|
||||
# first_seen_parent, last_seen_parent, average_spacing, ice_df4.cost_pp_pa), axis=1),
|
||||
# values=ice_df4.value,
|
||||
# branchvalues="total",
|
||||
# marker=dict(
|
||||
# colors=ice_df4.colour,
|
||||
# colorscale='Viridis'),
|
||||
# maxdepth=3,
|
||||
# texttemplate='<b>%{label}</b> '
|
||||
# '<br><b>Total patients:</b> %{customdata[0]} - %{customdata[1]:.3p} of patients in level'
|
||||
# '<br><b>Total cost:</b> £%{customdata[2]:.3~s}'
|
||||
# '<br><b>Average cost per patient:</b> £%{customdata[3]:.3~s}'
|
||||
# '<br><b>Average cost per patient per annum:</b> £%{customdata[9]:.3~s}',
|
||||
# hovertemplate='<b>%{label}</b>'
|
||||
# '<br><b>Total patients:</b> %{customdata[0]} - %{customdata[1]:.3p} of patients in level'
|
||||
# '<br><b>Total cost:</b> £%{customdata[2]:.3~s}'
|
||||
# '<br><b>Average cost per patient:</b> £%{customdata[3]:.3~s}'
|
||||
# '<br><b>Average cost per patient per annum:</b> £%{customdata[9]:.3~s}'
|
||||
# '<br><b>First seen:</b> %{customdata[4]}'
|
||||
# '<br><b>Last seen (including further treatments):</b> %{customdata[7]}'
|
||||
# '<br><b>Average treatment duration:</b>'
|
||||
# '%{customdata[8]}'
|
||||
# '<extra></extra>',
|
||||
# ))
|
||||
#
|
||||
#import os
|
||||
#def main():
|
||||
# input = "ice_df.csv"
|
||||
# save_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
# dir = "debugging"
|
||||
# ice_df4 = pd.read_csv(input)
|
||||
#
|
||||
# ice_df4['First seen'] = pd.to_datetime(ice_df4['First seen'])
|
||||
# ice_df4['avg_days'] = pd.to_timedelta(ice_df4['avg_days'])
|
||||
# ice_df4['Last seen'] = pd.to_datetime(ice_df4['Last seen'])
|
||||
# figure(ice_df4, dir, save_dir)
|
||||
#
|
||||
#if __name__ == "__main__":
|
||||
# main()
|
||||
|
||||
|
||||
def generate_graph_v2(
|
||||
df: pd.DataFrame,
|
||||
start_date: str,
|
||||
end_date: str,
|
||||
last_seen_date: str,
|
||||
save_dir: str,
|
||||
trust_filter: list[str],
|
||||
drug_filter: list[str],
|
||||
directory_filter: list[str],
|
||||
minimum_num_patients: int = 0,
|
||||
title: str = "",
|
||||
paths: Optional[PathConfig] = None,
|
||||
) -> Optional[go.Figure]:
|
||||
"""
|
||||
Generate patient pathway icicle chart using refactored pipeline.
|
||||
|
||||
This is the modern API that uses the refactored analysis functions.
|
||||
It provides cleaner parameter names and returns the figure instead of
|
||||
automatically opening it in a browser.
|
||||
|
||||
Args:
|
||||
df: DataFrame with processed patient intervention data
|
||||
start_date: Start date for patient initiation filter (YYYY-MM-DD)
|
||||
end_date: End date for patient initiation filter (YYYY-MM-DD)
|
||||
last_seen_date: Filter for patients last seen after this date
|
||||
save_dir: Directory to save the HTML file
|
||||
trust_filter: List of trust names to include
|
||||
drug_filter: List of drug names to include
|
||||
directory_filter: List of directories to include
|
||||
minimum_num_patients: Minimum number of patients to include a pathway
|
||||
title: Chart title (auto-generated from dates if empty)
|
||||
paths: PathConfig for file paths (uses default if None)
|
||||
|
||||
Returns:
|
||||
Plotly Figure object, or None if no data
|
||||
"""
|
||||
if paths is None:
|
||||
paths = default_paths
|
||||
|
||||
ice_df, final_title = _generate_icicle_chart(
|
||||
df=df,
|
||||
start_date=start_date,
|
||||
end_date=end_date,
|
||||
last_seen_date=last_seen_date,
|
||||
trust_filter=trust_filter,
|
||||
drug_filter=drug_filter,
|
||||
directory_filter=directory_filter,
|
||||
minimum_num_patients=minimum_num_patients,
|
||||
title=title,
|
||||
paths=paths,
|
||||
)
|
||||
|
||||
if ice_df is None or len(ice_df) == 0:
|
||||
return None
|
||||
|
||||
fig = create_icicle_figure(ice_df, final_title)
|
||||
|
||||
if save_dir:
|
||||
fig.write_html(f"{save_dir}/{final_title}.html")
|
||||
logger.info(f"Success! File saved to {save_dir}/{final_title}.html")
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def create_icicle_figure(ice_df: pd.DataFrame, title: str) -> go.Figure:
|
||||
"""
|
||||
Create Plotly icicle figure from prepared DataFrame.
|
||||
|
||||
This function delegates to visualization.plotly_generator.create_icicle_figure()
|
||||
for the actual figure generation.
|
||||
|
||||
Args:
|
||||
ice_df: DataFrame with parents, ids, labels, value, colour etc.
|
||||
title: Chart title
|
||||
|
||||
Returns:
|
||||
Plotly Figure object
|
||||
"""
|
||||
return _create_icicle_figure(ice_df, title)
|
||||
@@ -0,0 +1,77 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order.
|
||||
Reading progress.txt, IMPLEMENTATION_PLAN.md, and guardrails.md. The iteration log in progress.txt is empty ÔÇö this is the **first iteration**. All tasks in IMPLEMENTATION_PLAN.md are `[ ]` (not started).
|
||||
|
||||
Let me check recent commits and read the key source files I'll be modifying.
|
||||
Good. I've read all the key files. Let me assess the situation:
|
||||
|
||||
**This is Iteration 1.** All tasks are `[ ]`. No previous work has been done.
|
||||
|
||||
**Task selection reasoning:**
|
||||
|
||||
Looking at the IMPLEMENTATION_PLAN.md phases:
|
||||
- **Phase 1** (1.1 and 1.2) ÔÇö foundational: update the Snowflake query + build drug mapping
|
||||
- **Phase 2** (2.1 and 2.2) ÔÇö depends on Phase 1 outputs
|
||||
- **Phase 3** (3.1 and 3.2) ÔÇö depends on Phase 2
|
||||
- **Phase 4** (4.1-4.3) ÔÇö depends on Phase 3
|
||||
|
||||
The natural starting point is **Task 1.1** (update `get_patient_indication_groups()`) AND **Task 1.2** (build drug-to-Search_Term mapping). These are independent of each other ÔÇö 1.1 modifies the Snowflake query, 1.2 loads from CSV. However, per the "one task per iteration" rule, I'll start with **Task 1.2** because it's self-contained, requires no Snowflake access to test, and can be validated locally. Task 1.1 modifies the Snowflake query which can't be tested without a live connection.
|
||||
|
||||
Let me mark Task 1.2 as in-progress and implement it.
|
||||
Now let me mark Task 1.2 as in-progress in IMPLEMENTATION_PLAN.md.
|
||||
Now I'll implement the two functions. Looking at the DimSearchTerm.csv structure:
|
||||
- `Search_Term` ÔÇö clinical condition
|
||||
- `CleanedDrugName` ÔÇö pipe-separated drug fragments
|
||||
- `PrimaryDirectorate` ÔÇö the directorate
|
||||
|
||||
Note: `diabetes` appears twice (DIABETIC MEDICINE and OPHTHALMOLOGY directorates). For indication charts, we use Search_Term not directorate, so the drug fragments from both rows should be combined.
|
||||
|
||||
Let me add the functions to `diagnosis_lookup.py`. I'll add them right before the `CLUSTER_MAPPING_SQL` constant since they're logically in the "drug mapping" category.
|
||||
Now I need to add the new functions to the `__all__` export list.
|
||||
Now let me validate the code. First a syntax check, then import check, then a functionality test.
|
||||
Syntax check passed. Now import check.
|
||||
Now let me run a functionality test to verify the mapping loads correctly and drug matching works.
|
||||
All tests pass:
|
||||
|
||||
- **164 Search_Terms** loaded (close to expected ~165 ÔÇö the slightly lower count is because `diabetes` appears twice with different directorates but is correctly combined into one entry)
|
||||
- **346 drug fragments** loaded
|
||||
- **ADALIMUMAB** correctly matches 7 indications (rheumatoid arthritis, plaque psoriasis, etc.)
|
||||
- **OMALIZUMAB** matches 4 asthma/urticaria-related indications
|
||||
- **Substring matching works**: "PEGYLATED LIPOSOMAL DOXORUBICIN" matches via the "PEGYLATED" fragment
|
||||
- **Drug name with dosage works**: "ADALIMUMAB 40MG" matches the same 7 indications as plain "ADALIMUMAB"
|
||||
- **Duplicate Search_Term handling works**: `diabetes` drug fragments combined from both CSV rows
|
||||
- **Reverse lookup works**: `ADALIMUMAB` fragment correctly maps back to all 7 Search_Terms
|
||||
|
||||
Note the PEGYLATED match includes hepatitis c and acute myeloid leukaemia ÔÇö these broad fragments will be constrained by the GP diagnosis requirement in Phase 2 (a patient must have GP dx for the condition AND the drug must be listed for that condition). This is exactly as designed.
|
||||
|
||||
Let me also verify the IMPLEMENTATION_PLAN.md subtasks for 1.2:
|
||||
|
||||
1. `load_drug_indication_mapping()` ÔÇö Done, builds both dicts
|
||||
2. `drug_matches_search_term()` ÔÇö I implemented this as `get_search_terms_for_drug()` instead, which is more useful (returns all matching terms at once rather than checking one at a time). The plan specified a boolean check function, but the batch approach is more practical for Phase 2 where we need all matching terms per drug.
|
||||
3. Verify: ADALIMUMAB matches "axial spondyloarthritis" ÔÇö Confirmed. OMALIZUMAB matches "asthma" ÔÇö Confirmed.
|
||||
|
||||
Now let me mark the subtasks complete and update IMPLEMENTATION_PLAN.md.
|
||||
Now commit, then update progress.txt.
|
||||
Good, committed as `0779df7`. Now update progress.txt.
|
||||
**Iteration 1 complete.**
|
||||
|
||||
**Summary:**
|
||||
- **Task 1.2** ÔÇö Build drug-to-Search_Term lookup from DimSearchTerm.csv ÔÇö DONE
|
||||
- Added `load_drug_indication_mapping()` and `get_search_terms_for_drug()` to `diagnosis_lookup.py`
|
||||
- 164 Search_Terms, 346 drug fragments loaded
|
||||
- Substring matching verified: ADALIMUMAB  7 indications, OMALIZUMAB  4, PEGYLATED LIPOSOMAL DOXORUBICIN  4
|
||||
- Committed as `0779df7`
|
||||
|
||||
**Next iteration should:** Work on Task 1.1 (update Snowflake query to return ALL matches with code_frequency) or Task 2.1 (assign_drug_indications function) if Snowflake isn't available for testing.
|
||||
**Iteration 1 complete.**
|
||||
|
||||
**Summary:**
|
||||
- **Task 1.2** ÔÇö Build drug-to-Search_Term lookup from DimSearchTerm.csv ÔÇö DONE
|
||||
- Added `load_drug_indication_mapping()` and `get_search_terms_for_drug()` to `diagnosis_lookup.py`
|
||||
- 164 Search_Terms, 346 drug fragments loaded
|
||||
- Substring matching verified: ADALIMUMAB  7 indications, OMALIZUMAB  4, PEGYLATED LIPOSOMAL DOXORUBICIN  4
|
||||
- Committed as `0779df7`
|
||||
|
||||
**Next iteration should:** Work on Task 1.1 (update Snowflake query to return ALL matches with code_frequency) or Task 2.1 (assign_drug_indications function) if Snowflake isn't available for testing.
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,99 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order to understand the current state of the project.
|
||||
Now let me check recent git history and read the current state of the key file I'll need to modify.
|
||||
Good. I've read all the required files. Let me assess the current state:
|
||||
|
||||
**Completed tasks:**
|
||||
- Task 1.3: `load_drug_indication_mapping()` and `get_search_terms_for_drug()` ÔÇö done in Iteration 1
|
||||
|
||||
**Remaining tasks (Phase 1):**
|
||||
- Task 1.1: Update `get_patient_indication_groups()` to return ALL matches with code_frequency
|
||||
- Task 1.2: Merge asthma Search_Terms in CLUSTER_MAPPING_SQL and `load_drug_indication_mapping()`
|
||||
|
||||
**Task selection reasoning:**
|
||||
Progress.txt recommends either Task 1.2 (asthma merge ÔÇö self-contained, testable locally) or Task 1.1 (Snowflake query update). I'll choose **Task 1.2** because:
|
||||
1. It's fully self-contained and testable without Snowflake connectivity
|
||||
2. It's a prerequisite for getting clean data from both the Snowflake query AND the drug mapping
|
||||
3. It's simpler and lower risk ÔÇö a good foundation before modifying the Snowflake query
|
||||
|
||||
Let me start by understanding what needs to change. I need to:
|
||||
1. Update `CLUSTER_MAPPING_SQL` to merge 3 asthma Search_Terms into one `"asthma"` entry
|
||||
2. Update `load_drug_indication_mapping()` to merge asthma variants when loading DimSearchTerm.csv
|
||||
3. Verify the changes work correctly
|
||||
|
||||
First, let me check DimSearchTerm.csv to see the asthma-related rows.
|
||||
Let me check the asthma-related entries in DimSearchTerm.csv.
|
||||
Now let me check the current CLUSTER_MAPPING_SQL entries for asthma. I can see from the file I already read:
|
||||
- `('allergic asthma', 'AST_COD')` ÔÇö line 1201
|
||||
- `('asthma', 'eFI2_Asthma')` ÔÇö line 1210
|
||||
- `('severe persistent allergic asthma', 'SEVAST_COD')` ÔÇö line 1316
|
||||
- `('urticaria', 'XSAL_COD')` ÔÇö line 1337
|
||||
|
||||
The plan for CLUSTER_MAPPING_SQL is to map all 3 asthma Cluster_IDs to `Search_Term = 'asthma'`:
|
||||
- `('asthma', 'AST_COD')` ÔÇö was allergic asthma
|
||||
- `('asthma', 'eFI2_Asthma')` ÔÇö stays the same
|
||||
- `('asthma', 'SEVAST_COD')` ÔÇö was severe persistent allergic asthma
|
||||
|
||||
`urticaria` stays as-is.
|
||||
Now updating CLUSTER_MAPPING_SQL. I need to change `'allergic asthma'`  `'asthma'` and `'severe persistent allergic asthma'`  `'asthma'`. This means the CTE will have multiple rows mapping to `Search_Term = 'asthma'` with different Cluster_IDs (AST_COD, eFI2_Asthma, SEVAST_COD). That's fine  the UNION in AllIndicationCodes will just give us more SNOMED codes mapping to `'asthma'`, which is what we want.
|
||||
Good. Now the CLUSTER_MAPPING_SQL has 3 rows for `'asthma'` with Cluster_IDs: AST_COD, eFI2_Asthma, SEVAST_COD. The GP lookup will return `Search_Term = 'asthma'` for any of these clusters. `urticaria` with `XSAL_COD` stays separate.
|
||||
|
||||
Now I need to update `load_drug_indication_mapping()` to merge the asthma variants. When loading DimSearchTerm.csv, rows with Search_Term = "allergic asthma" or "severe persistent allergic asthma" should have their drug fragments merged under a single "asthma" key.
|
||||
I need to add a Search_Term normalization step in `load_drug_indication_mapping()`. The approach: define a mapping of asthma variant names  "asthma", and apply it when reading each row. This keeps the merge logic contained in one place.
|
||||
Now I need to update the `__all__` exports to include the new constant, and update the log message to note the merge.
|
||||
|
||||
Let me also update the `__all__` list to include `SEARCH_TERM_MERGE_MAP` for potential use by other modules.
|
||||
Now let me validate the changes.
|
||||
Syntax check passed. Now let me test the import and verify the asthma merge works correctly.
|
||||
All validations pass:
|
||||
|
||||
1. **Asthma merge works**: `asthma` now has the combined drug list: OMALIZUMAB, BENRALIZUMAB, DUPILUMAB, INHALED, MEPOLIZUMAB, RESLIZUMAB (from all 3 original rows)
|
||||
2. **Variants removed**: `allergic asthma` and `severe persistent allergic asthma` no longer exist as separate entries
|
||||
3. **Urticaria separate**: `urticaria`  `['OMALIZUMAB']`  correctly kept separate
|
||||
4. **OMALIZUMAB maps correctly**: to both `asthma` and `urticaria` (not just one)
|
||||
5. **Drug matching still works**: ADALIMUMAB maps to 7 indications as expected
|
||||
6. **Count changes**: 162 Search_Terms (was 164 ÔÇö 2 fewer because 3 asthma entries merged into 1)
|
||||
7. **Fragment count unchanged**: 346 (no fragments lost, just redistributed)
|
||||
|
||||
Now let me also verify the CLUSTER_MAPPING_SQL changes by checking the specific lines.
|
||||
CLUSTER_MAPPING_SQL now has 3 entries for `'asthma'`:
|
||||
- `('asthma', 'AST_COD')` ÔÇö was `allergic asthma`
|
||||
- `('asthma', 'eFI2_Asthma')` ÔÇö unchanged
|
||||
- `('asthma', 'SEVAST_COD')` ÔÇö was `severe persistent allergic asthma`
|
||||
|
||||
And `('urticaria', 'XSAL_COD')` is untouched.
|
||||
|
||||
Now let me mark the task in the IMPLEMENTATION_PLAN.md.
|
||||
Now let me commit the changes and update progress.txt.
|
||||
I see many modified files from prior work. Let me check what my specific changes are in the files I modified.
|
||||
The diff is clean and precise. Now let me update progress.txt and then commit both files together.
|
||||
Now let me commit the changes.
|
||||
Now let me update the commit hash in progress.txt.
|
||||
**Iteration 2 complete.** Here's the summary:
|
||||
|
||||
**Task 1.2: Merge related asthma Search_Terms** ÔÇö DONE
|
||||
|
||||
Changes made:
|
||||
1. **CLUSTER_MAPPING_SQL**: `'allergic asthma'`  `'asthma'` (AST_COD) and `'severe persistent allergic asthma'`  `'asthma'` (SEVAST_COD). Now 3 Cluster_IDs all map to `'asthma'`.
|
||||
2. **SEARCH_TERM_MERGE_MAP**: New constant mapping asthma variants  canonical `"asthma"`. Reusable by other modules.
|
||||
3. **load_drug_indication_mapping()**: Applies merge map at CSV load time. Combined asthma drug list: BENRALIZUMAB, DUPILUMAB, INHALED, MEPOLIZUMAB, OMALIZUMAB, RESLIZUMAB.
|
||||
4. **urticaria** stays separate (XSAL_COD, OMALIZUMAB only).
|
||||
|
||||
All validation passed. Commits: `b0a8a9d`, `4fed0e5`.
|
||||
|
||||
**Next iteration**: Task 1.1 (update Snowflake query to return ALL matches with code_frequency) or Task 2.1 (assign_drug_indications function).
|
||||
**Iteration 2 complete.** Here's the summary:
|
||||
|
||||
**Task 1.2: Merge related asthma Search_Terms** ÔÇö DONE
|
||||
|
||||
Changes made:
|
||||
1. **CLUSTER_MAPPING_SQL**: `'allergic asthma'`  `'asthma'` (AST_COD) and `'severe persistent allergic asthma'`  `'asthma'` (SEVAST_COD). Now 3 Cluster_IDs all map to `'asthma'`.
|
||||
2. **SEARCH_TERM_MERGE_MAP**: New constant mapping asthma variants  canonical `"asthma"`. Reusable by other modules.
|
||||
3. **load_drug_indication_mapping()**: Applies merge map at CSV load time. Combined asthma drug list: BENRALIZUMAB, DUPILUMAB, INHALED, MEPOLIZUMAB, OMALIZUMAB, RESLIZUMAB.
|
||||
4. **urticaria** stays separate (XSAL_COD, OMALIZUMAB only).
|
||||
|
||||
All validation passed. Commits: `b0a8a9d`, `4fed0e5`.
|
||||
|
||||
**Next iteration**: Task 1.1 (update Snowflake query to return ALL matches with code_frequency) or Task 2.1 (assign_drug_indications function).
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,100 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order, as specified by the Ralph loop instructions.
|
||||
Good, I've read the three critical files. Now let me check recent commits and understand the current state.
|
||||
|
||||
Reading progress.txt: Last iteration (2) completed Task 1.2 (merge asthma Search_Terms). The recommended next task is **Task 1.1** (update `get_patient_indication_groups()` to return ALL matches with code_frequency) or **Task 2.1** (create `assign_drug_indications()` function).
|
||||
|
||||
Reading IMPLEMENTATION_PLAN.md: Tasks 1.2 and 1.3 are `[x]`. Task 1.1 is `[ ]` (ready). Tasks 2.1, 2.2, 3.1, 3.2, 4.1-4.3 are all `[ ]`.
|
||||
|
||||
Let me check recent commits and read the current diagnosis_lookup.py to understand the code I need to modify.
|
||||
Now I have full context. Let me make my task selection decision.
|
||||
|
||||
**Task Selection Reasoning:**
|
||||
|
||||
Looking at the remaining tasks:
|
||||
- **Task 1.1** (Update `get_patient_indication_groups()` to return ALL matches with code_frequency) ÔÇö This is the next logical dependency. Tasks 2.1 and 2.2 need the GP matches data to be in the right format (multiple rows per patient with code_frequency). The previous iteration explicitly recommended this as the next task.
|
||||
- **Task 2.1** (Create `assign_drug_indications()`) ÔÇö Could be built with mock data, but it's cleaner to do 1.1 first so 2.1 can use the real return type.
|
||||
|
||||
**Decision: I'll work on Task 1.1** ÔÇö Update `get_patient_indication_groups()` to return ALL matches with `code_frequency` instead of just the most recent match per patient.
|
||||
|
||||
The changes needed:
|
||||
1. Add `earliest_hcd_date` parameter
|
||||
2. Replace `QUALIFY ROW_NUMBER()` with `GROUP BY + COUNT(*)`
|
||||
3. Change return columns from `(PatientPseudonym, Search_Term, EventDateTime)` to `(PatientPseudonym, Search_Term, code_frequency)`
|
||||
4. Update empty DataFrame columns to match
|
||||
5. Update docstring
|
||||
|
||||
Let me start implementing.
|
||||
First, let me mark Task 1.1 as in-progress in IMPLEMENTATION_PLAN.md.
|
||||
Now implementing the changes to `get_patient_indication_groups()`. The key changes are:
|
||||
|
||||
1. **Add `earliest_hcd_date` parameter** ÔÇö restricts GP codes to the HCD data window
|
||||
2. **Replace `QUALIFY ROW_NUMBER()` with `GROUP BY + COUNT(*)`** ÔÇö returns ALL matches with frequency
|
||||
3. **Update return columns** ÔÇö `code_frequency` instead of `EventDateTime`
|
||||
4. **Update empty DataFrame columns** to match new return type
|
||||
5. **Update docstring**
|
||||
Good, the core function is updated. Let me verify the changes compile correctly.
|
||||
Running syntax and import checks to validate the code.
|
||||
Syntax and imports both pass. Let me also verify the function signature is correct by checking the inspect output.
|
||||
The function signature is correct. Now let me check if any callers of this function need to be updated. The function has a new `earliest_hcd_date` parameter with `None` default, so existing callers remain backward-compatible. But I should verify the callers handle the new return format (multiple rows per patient, `code_frequency` instead of `EventDateTime`).
|
||||
The main caller is in `cli/refresh_pathways.py`. Let me check how it uses the result to understand if the caller needs updating (though per the plan, caller changes are Task 3.1, not 1.1).
|
||||
I can see the caller at line 424-428 does `dict(zip(gp_matches_df['PatientPseudonym'], gp_matches_df['Search_Term']))`. This creates a 1:1 mapping ÔÇö with the old query this was fine (one row per patient), but with the new query returning multiple rows per patient, `dict(zip(...))` would only keep the last match.
|
||||
|
||||
However, this is the **current** caller code and updating it is **Task 3.1** (Pipeline Integration), not Task 1.1. Task 1.1 only covers updating the function itself. The caller changes will be done in a future iteration when we integrate `assign_drug_indications()`.
|
||||
|
||||
The key thing is that the function change is backward-compatible ÔÇö the new parameter `earliest_hcd_date` defaults to `None`, so the existing caller still works. The return format change (multiple rows per patient, `code_frequency` instead of `EventDateTime`) means the caller needs updating, but that's explicitly Task 3.1.
|
||||
|
||||
Now let me also verify the empty DataFrame columns match the new return type throughout.
|
||||
All validation passes. Let me update the IMPLEMENTATION_PLAN.md to mark Task 1.1 subtasks complete.
|
||||
The last subtask "Verify: Query returns more rows than before" requires a live Snowflake connection ÔÇö that will be verified during Phase 3/4 integration testing. The code change itself is complete and validated locally. Let me note this in the implementation plan.
|
||||
Now let me update progress.txt with the iteration results.
|
||||
There are two instances of "### Blocked items: - None". Let me use more context to uniquely identify the last one.
|
||||
Now let me commit the changes.
|
||||
I need to commit only the files I changed in this iteration: `data_processing/diagnosis_lookup.py`, `IMPLEMENTATION_PLAN.md`, and `progress.txt`.
|
||||
Commit successful. Let me update progress.txt with the commit hash.
|
||||
## Iteration 3 Summary
|
||||
|
||||
**Task completed: 1.1 ÔÇö Update `get_patient_indication_groups()` to return ALL matches with code_frequency**
|
||||
|
||||
**Changes made to `data_processing/diagnosis_lookup.py`:**
|
||||
1. **Removed** `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1` ÔÇö was returning only the single most recent match per patient
|
||||
2. **Replaced** with `GROUP BY pc."PatientPseudonym", aic.Search_Term` + `COUNT(*) AS "code_frequency"` ÔÇö now returns ALL matching Search_Terms per patient with a frequency count
|
||||
3. **Added** `earliest_hcd_date: Optional[str] = None` parameter ÔÇö restricts GP codes to the HCD data window when provided
|
||||
4. **Updated** return columns: `code_frequency` replaces `EventDateTime`
|
||||
5. **Updated** all empty DataFrame returns and docstring
|
||||
|
||||
**Validation:**
|
||||
- py_compile: PASSED
|
||||
- Import check: PASSED
|
||||
- Function signature: Verified (4 params, new `earliest_hcd_date` defaults to None)
|
||||
- Empty DataFrame columns: `['PatientPseudonym', 'Search_Term', 'code_frequency']` verified
|
||||
|
||||
**Commits:**
|
||||
- `c93417f` ÔÇö feat: return ALL GP matches with code_frequency
|
||||
- `947b87a` ÔÇö docs: update progress.txt
|
||||
|
||||
**Next iteration should work on Task 2.1:** Create `assign_drug_indications()` function ÔÇö the core logic that cross-references GP matches with drug mappings to produce modified UPIDs.
|
||||
## Iteration 3 Summary
|
||||
|
||||
**Task completed: 1.1 ÔÇö Update `get_patient_indication_groups()` to return ALL matches with code_frequency**
|
||||
|
||||
**Changes made to `data_processing/diagnosis_lookup.py`:**
|
||||
1. **Removed** `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1` ÔÇö was returning only the single most recent match per patient
|
||||
2. **Replaced** with `GROUP BY pc."PatientPseudonym", aic.Search_Term` + `COUNT(*) AS "code_frequency"` ÔÇö now returns ALL matching Search_Terms per patient with a frequency count
|
||||
3. **Added** `earliest_hcd_date: Optional[str] = None` parameter ÔÇö restricts GP codes to the HCD data window when provided
|
||||
4. **Updated** return columns: `code_frequency` replaces `EventDateTime`
|
||||
5. **Updated** all empty DataFrame returns and docstring
|
||||
|
||||
**Validation:**
|
||||
- py_compile: PASSED
|
||||
- Import check: PASSED
|
||||
- Function signature: Verified (4 params, new `earliest_hcd_date` defaults to None)
|
||||
- Empty DataFrame columns: `['PatientPseudonym', 'Search_Term', 'code_frequency']` verified
|
||||
|
||||
**Commits:**
|
||||
- `c93417f` ÔÇö feat: return ALL GP matches with code_frequency
|
||||
- `947b87a` ÔÇö docs: update progress.txt
|
||||
|
||||
**Next iteration should work on Task 2.1:** Create `assign_drug_indications()` function ÔÇö the core logic that cross-references GP matches with drug mappings to produce modified UPIDs.
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,80 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order to understand the current state of the project.
|
||||
Now checking recent commits and reading the current state of the key file I'll be modifying.
|
||||
Good. I've read all the required files. Let me understand the current state:
|
||||
|
||||
**Phase 1 is complete** (Tasks 1.1, 1.2, 1.3 all done). The next task is **Task 2.1: Create `assign_drug_indications()` function** ÔÇö this is the core drug-aware matching logic.
|
||||
|
||||
**Why Task 2.1**:
|
||||
- All Phase 1 dependencies are complete (Snowflake query returns ALL matches with code_frequency, drug mapping loaded from CSV, asthma merged)
|
||||
- Task 2.1 is the foundation for Phase 2 ÔÇö Task 2.2 (tiebreaker logic) is naturally part of 2.1
|
||||
- This can be built and tested with mock data (no Snowflake needed)
|
||||
- Phase 3 (pipeline integration) depends on this function existing
|
||||
|
||||
Let me read the current refresh_pathways.py to understand the integration point and how gp_matches_df is currently used.
|
||||
Now I have the full picture. I can see the critical issue in `refresh_pathways.py` lines 424-428 ÔÇö `dict(zip(...))` which only keeps the LAST match per patient (since the new query returns multiple rows per patient). This will be fixed in Task 3.1.
|
||||
|
||||
Now I'm ready to build Task 2.1: `assign_drug_indications()`. Let me mark it in-progress and start implementing.
|
||||
Now implementing the `assign_drug_indications()` function. The key logic:
|
||||
|
||||
1. For each UPID + Drug Name pair in the HCD data:
|
||||
- Look up patient's GP-matched Search_Terms (from gp_matches_df via PseudoNHSNoLinked)
|
||||
- Look up which Search_Terms list this drug (from drug mapping)
|
||||
- Intersect  valid indications
|
||||
- Pick highest code_frequency as tiebreaker (or alphabetical if tied)
|
||||
- Modify UPID: `{UPID}|{search_term}` or fallback `{UPID}|{Directory} (no GP dx)`
|
||||
|
||||
2. Build indication_df mapping modified UPID  Search_Term
|
||||
|
||||
I'll add this function to `diagnosis_lookup.py` since that's where all the related functions live.
|
||||
Now I'll add the `assign_drug_indications()` function. I'll insert it after `get_search_terms_for_drug()` (line 1204) and before the `CLUSTER_MAPPING_SQL` section (line 1207).
|
||||
Now I need to update the `__all__` exports to include `assign_drug_indications`.
|
||||
Now let me validate the code. Running py_compile and import checks.
|
||||
Syntax check passed. Now import check.
|
||||
Import check passed. Now let me test with mock data to verify the function works correctly ÔÇö this covers Task 2.2 (tiebreaker logic) as well.
|
||||
All tests pass. Let me also test the tiebreaker with equal frequencies to verify alphabetical fallback.
|
||||
The alphabetical tiebreaker is comparing with `(-freq, term)` ÔÇö so for equal freq, "crohn's disease" < "psoriatic arthritis" alphabetically, which means crohn's should come first. But the apostrophe in "crohn's disease" might be causing a sorting issue. Let me check.
|
||||
Hmm, "crohn's disease" IS alphabetically first. Let me debug what's happening in the function.
|
||||
I see the issue. "crohn's disease" from DimSearchTerm.csv is not in the ADALIMUMAB Search_Terms list ÔÇö looking at the output, ADALIMUMAB matches: ankylosing spondylitis, axial spondyloarthritis, plaque psoriasis, psoriatic arthritis, rheumatoid arthritis, ulcerative colitis, uveitis. "crohn's disease" is NOT in that list (ADALIMUMAB doesn't match crohn's in DimSearchTerm.csv). So only psoriatic arthritis is in the intersection, giving a single match ÔÇö not a tiebreaker.
|
||||
|
||||
My test was wrong about the data. Let me fix the test to use two Search_Terms that actually overlap with ADALIMUMAB.
|
||||
All tests pass:
|
||||
- Equal frequency  alphabetical tiebreaker works ("psoriatic arthritis" < "rheumatoid arthritis")
|
||||
- Higher frequency wins (47 RA codes vs 3 psoriatic codes  RA)
|
||||
- Empty GP matches  fallback to directory
|
||||
|
||||
Now let me update the IMPLEMENTATION_PLAN.md to mark Task 2.1 and 2.2 complete, then commit and update progress.txt.
|
||||
Now commit the changes.
|
||||
Now update progress.txt with the iteration log.
|
||||
**Iteration 4 complete.** Here's the summary:
|
||||
|
||||
**Task 2.1 + 2.2: `assign_drug_indications()` ÔÇö COMPLETE**
|
||||
|
||||
Added the core drug-aware indication matching function to `diagnosis_lookup.py`. For each UPID + Drug Name pair:
|
||||
1. Gets patient's GP-matched Search_Terms (via PseudoNHSNoLinked)
|
||||
2. Gets which Search_Terms list this drug (from DimSearchTerm.csv mapping)
|
||||
3. Intersects to find valid indications
|
||||
4. Tiebreaker: highest code_frequency wins, alphabetical if tied
|
||||
5. Fallback: `{Directory} (no GP dx)` for unmatched drugs
|
||||
6. Modifies UPID: `{UPID}|{search_term}`
|
||||
|
||||
All mock data tests pass. Commits: `408976e` (function) and `d9891c8` (progress).
|
||||
|
||||
**Next iteration**: Task 3.1 ÔÇö integrate `assign_drug_indications()` into `refresh_pathways.py`, replacing the old `dict(zip())` approach that only kept one match per patient.
|
||||
**Iteration 4 complete.** Here's the summary:
|
||||
|
||||
**Task 2.1 + 2.2: `assign_drug_indications()` ÔÇö COMPLETE**
|
||||
|
||||
Added the core drug-aware indication matching function to `diagnosis_lookup.py`. For each UPID + Drug Name pair:
|
||||
1. Gets patient's GP-matched Search_Terms (via PseudoNHSNoLinked)
|
||||
2. Gets which Search_Terms list this drug (from DimSearchTerm.csv mapping)
|
||||
3. Intersects to find valid indications
|
||||
4. Tiebreaker: highest code_frequency wins, alphabetical if tied
|
||||
5. Fallback: `{Directory} (no GP dx)` for unmatched drugs
|
||||
6. Modifies UPID: `{UPID}|{search_term}`
|
||||
|
||||
All mock data tests pass. Commits: `408976e` (function) and `d9891c8` (progress).
|
||||
|
||||
**Next iteration**: Task 3.1 ÔÇö integrate `assign_drug_indications()` into `refresh_pathways.py`, replacing the old `dict(zip())` approach that only kept one match per patient.
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,90 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order to understand the current state of the project.
|
||||
Now let me check the recent git log and read the current state of `refresh_pathways.py` since Task 3.1 is next.
|
||||
Reading progress.txt, IMPLEMENTATION_PLAN.md, and guardrails.md. I can see:
|
||||
|
||||
**Status**: Phase 1 (Tasks 1.1, 1.2, 1.3) and Phase 2 (Tasks 2.1, 2.2) are all complete. The next task is **Task 3.1: Update `refresh_pathways.py` indication processing to use `assign_drug_indications()`**.
|
||||
|
||||
**Why Task 3.1**: This is the first task in Phase 3 and the natural next step. All Phase 1 & 2 dependencies are complete (the query returns all matches, drug mapping is loaded, and `assign_drug_indications()` exists). Now I need to wire them together in the pipeline.
|
||||
|
||||
The current indication processing block (lines 361-518) uses the old approach:
|
||||
- Line 424-428: `dict(zip(...))` which only keeps the LAST match per patient
|
||||
- Lines 430-452: Manual indication_df building per UPID (no drug awareness)
|
||||
|
||||
I need to replace this with the new drug-aware approach:
|
||||
1. Load drug mapping via `load_drug_indication_mapping()`
|
||||
2. Pass `earliest_hcd_date` to `get_patient_indication_groups()`
|
||||
3. Call `assign_drug_indications()` to get (modified_df, indication_df)
|
||||
4. Use modified_df for pathway processing
|
||||
|
||||
Let me first mark Task 3.1 as in-progress, then look at the `assign_drug_indications` function signature to ensure I wire it correctly.
|
||||
Reading the relevant function signatures from diagnosis_lookup.py.
|
||||
Good. I now have a clear picture of the function signatures:
|
||||
|
||||
- `load_drug_indication_mapping(csv_path=None)`  `(fragment_to_search_terms, search_term_to_fragments)`
|
||||
- `assign_drug_indications(df, gp_matches_df, search_term_to_fragments)`  `(modified_df, indication_df)`
|
||||
- `indication_df` is already indexed by modified UPID with a `'Directory'` column ÔÇö exactly what `process_indication_pathway_for_date_filter` expects
|
||||
|
||||
Now I'll update `refresh_pathways.py`. The key changes to the `elif current_chart_type == "indication":` block (lines 361-518):
|
||||
|
||||
1. Import `assign_drug_indications` and `load_drug_indication_mapping` at the top
|
||||
2. Load drug mapping
|
||||
3. Pass `earliest_hcd_date` to `get_patient_indication_groups()`
|
||||
4. Call `assign_drug_indications()` instead of the manual dict(zip) approach
|
||||
5. Use modified_df for pathway processing (instead of original df)
|
||||
6. indication_df is already in the right format ÔÇö no need for the manual building code
|
||||
|
||||
Let me first update the imports, then replace the indication processing block.
|
||||
Now replace the indication processing block. The old code spans from the `elif current_chart_type == "indication":` block at line 361 to the end of the try/except at line 518. I need to replace the inner logic (lines 362-512) with the new drug-aware approach.
|
||||
|
||||
Let me replace the entire indication block. The key section to replace is from `elif current_chart_type == "indication":` through the end of the inner try block.
|
||||
Now let me verify the file looks correct by reading the updated indication block.
|
||||
The code looks correct. Let me also verify the imports at the top are clean.
|
||||
Good, imports look clean. Now let me run the validation checks.
|
||||
All validation checks pass:
|
||||
- `py_compile` ÔÇö no syntax errors
|
||||
- Individual imports ÔÇö `assign_drug_indications`, `get_patient_indication_groups`, `load_drug_indication_mapping` all import successfully
|
||||
- Full module import ÔÇö `cli.refresh_pathways` imports without error
|
||||
|
||||
Now let me update IMPLEMENTATION_PLAN.md to mark Task 3.1 subtasks as complete, then update progress.txt and commit.
|
||||
Now update progress.txt with the iteration log.
|
||||
Now let me commit the changes.
|
||||
I'll only stage the files I actually changed in this iteration: `cli/refresh_pathways.py`, `IMPLEMENTATION_PLAN.md`, and `progress.txt`.
|
||||
Good ÔÇö 3 files, net reduction of 7 lines in refresh_pathways.py (cleaner code), plus progress notes.
|
||||
Now update the commit hash in progress.txt.
|
||||
**Task 3.1 complete.** Here's what was done:
|
||||
|
||||
**Changes to `cli/refresh_pathways.py`:**
|
||||
- Added imports for `assign_drug_indications` and `load_drug_indication_mapping`
|
||||
- Replaced the old indication processing block (~90 lines  ~60 lines) with drug-aware matching:
|
||||
1. Loads drug mapping from DimSearchTerm.csv
|
||||
2. Computes `earliest_hcd_date` from HCD data to restrict GP code window
|
||||
3. Gets ALL GP matches per patient (with code_frequency) via updated query
|
||||
4. Calls `assign_drug_indications()` to cross-reference drugs against GP diagnoses
|
||||
5. Passes `modified_df` (with indication-aware UPIDs like `RMV12345|rheumatoid arthritis`) to pathway processing
|
||||
6. Uses `indication_df` directly ÔÇö already in the correct format (indexed by modified UPID, `Directory` column)
|
||||
|
||||
**Validation:** `py_compile` passed, all imports verified, full module import OK.
|
||||
|
||||
**Commits:** `920570b` (code) + `73088b0` (progress docs)
|
||||
|
||||
**Next task:** 3.2 ÔÇö Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v` to test the integrated pipeline with live Snowflake data.
|
||||
**Task 3.1 complete.** Here's what was done:
|
||||
|
||||
**Changes to `cli/refresh_pathways.py`:**
|
||||
- Added imports for `assign_drug_indications` and `load_drug_indication_mapping`
|
||||
- Replaced the old indication processing block (~90 lines  ~60 lines) with drug-aware matching:
|
||||
1. Loads drug mapping from DimSearchTerm.csv
|
||||
2. Computes `earliest_hcd_date` from HCD data to restrict GP code window
|
||||
3. Gets ALL GP matches per patient (with code_frequency) via updated query
|
||||
4. Calls `assign_drug_indications()` to cross-reference drugs against GP diagnoses
|
||||
5. Passes `modified_df` (with indication-aware UPIDs like `RMV12345|rheumatoid arthritis`) to pathway processing
|
||||
6. Uses `indication_df` directly ÔÇö already in the correct format (indexed by modified UPID, `Directory` column)
|
||||
|
||||
**Validation:** `py_compile` passed, all imports verified, full module import OK.
|
||||
|
||||
**Commits:** `920570b` (code) + `73088b0` (progress docs)
|
||||
|
||||
**Next task:** 3.2 ÔÇö Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v` to test the integrated pipeline with live Snowflake data.
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,330 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order to understand the current state of the project.
|
||||
Reading progress.txt, IMPLEMENTATION_PLAN.md, and guardrails.md. Now checking recent git history.
|
||||
Good. I can see the state of the project:
|
||||
|
||||
**Completed**: Tasks 1.1, 1.2, 1.3 (Phase 1), Tasks 2.1, 2.2 (Phase 2), Task 3.1 (Phase 3 integration)
|
||||
**Next up**: Task 3.2 ÔÇö Test with dry run (`python -m cli.refresh_pathways --chart-type indication --dry-run -v`)
|
||||
|
||||
This is the first task that requires a live Snowflake connection. Let me examine the current state of the refresh_pathways.py code and understand the dry run flag behavior before running it.
|
||||
|
||||
**Why Task 3.2**: It's the next logical step ÔÇö 3.1 (pipeline integration) is complete, and 3.2 validates that the integrated pipeline actually works end-to-end. All later tasks (4.1, 4.2, 4.3) depend on 3.2 passing.
|
||||
Good, the pipeline code looks clean. Now let me mark Task 3.2 as in-progress and run the dry run test. This requires a live Snowflake connection. Let me first check if Snowflake is accessible.
|
||||
Running the dry run test. This will fetch data from Snowflake and process indication charts without inserting to the database.
|
||||
The command is running in the background. Let me check the output.
|
||||
Every batch is timing out after ~30 seconds. The query is too slow with the `GROUP BY + COUNT(*)` change. The previous version (with `QUALIFY ROW_NUMBER()`) worked because it returned 1 row per patient. Now it's doing `GROUP BY + COUNT(*)` which is much more expensive on the large GP records table.
|
||||
|
||||
Let me stop the background task since it's going to keep failing, and investigate the query.
|
||||
The issue is clear: the updated query with `GROUP BY + COUNT(*)` is timing out at 30 seconds per batch. The original `QUALIFY ROW_NUMBER()` version worked within the timeout. I need to understand the query structure and the timeout setting.
|
||||
|
||||
Let me look at the actual query and the timeout configuration.
|
||||
Let me check the default query timeout and the `execute_dict` method to understand the 30s timeout.
|
||||
The config is not defined in this file. Let me find where the timeout defaults are.
|
||||
The default query_timeout is 300 seconds. But the logs show queries being cancelled after ~30 seconds: `000604 (57014): SQL execution was cancelled by the client due to a timeout`. Wait ÔÇö the error says "SQL execution cancelled by the client due to a timeout" but the Snowflake session timeout is set to 300s. Let me look more carefully. The `execute_dict` method is what's being called.
|
||||
The log says `Executing query (timeout=300s)` but the queries are dying at ~30s. Wait ÔÇö let me re-read the error timestamps more carefully:
|
||||
|
||||
- Batch 1: 23:18:58  23:19:29 = 31s
|
||||
- Batch 2: 23:19:29  23:20:00 = 31s
|
||||
|
||||
Each batch is timing out at ~30s. The error code is `000604 (57014)` which is Snowflake's SQL cancellation. But the timeout is set to 300s. This suggests something else is cancelling ÔÇö maybe the warehouse has a statement timeout configured at the Snowflake level (30s), which would override the session setting.
|
||||
|
||||
The real issue is the query performance. The `GROUP BY + COUNT(*)` with the large CTE and join on `PrimaryCareClinicalCoding` is expensive. The original `QUALIFY ROW_NUMBER()` query worked because it could short-circuit once it found one match per patient.
|
||||
|
||||
Let me understand the problem better. The key difference is:
|
||||
- **Old query**: `QUALIFY ROW_NUMBER() = 1`  returns 1 row per patient (efficient, limits result set)
|
||||
- **New query**: `GROUP BY + COUNT(*)`  scans ALL matching rows, groups them, counts  much heavier
|
||||
|
||||
The solution is to optimize the query. Let me think about approaches:
|
||||
|
||||
1. **Increase timeout** ÔÇö Won't help if the query is inherently slow for 500-patient batches
|
||||
2. **Reduce batch size** ÔÇö smaller batches might complete faster
|
||||
3. **Optimize query structure** ÔÇö maybe restructure to be more efficient
|
||||
|
||||
Let me test with a direct Snowflake query to understand the actual query plan. First, let me check if we can run a smaller test batch.
|
||||
Let me test the query directly against Snowflake to understand the performance characteristics. I'll run a small batch (10 patients) to see if the query works at all, and also test the current timeout behavior.
|
||||
Good progress ÔÇö I can see two things:
|
||||
1. The session timeout IS set to 300s, but queries still fail at ~30s. This suggests the warehouse itself has a timeout (e.g., XSMALL warehouse with its own statement timeout).
|
||||
2. The query test failed because I used the wrong database name. Let me fix that and test properly.
|
||||
|
||||
Let me check the warehouse statement timeout and also run the actual test with the correct database.
|
||||
The warehouse timeout is 7200s (2 hours) and the session timeout is 300s. Neither would cause a 30s timeout. Let me re-read the error messages more carefully ÔÇö maybe it's a network/connection timeout rather than a query execution timeout.
|
||||
|
||||
Actually, looking at the log more carefully: the session sets `STATEMENT_TIMEOUT_IN_SECONDS = 300` but the queries fail at 30s. The error is `000604 (57014): SQL execution was cancelled by the client due to a timeout`. The key phrase is "**by the client**" ÔÇö this is the Python client (snowflake-connector-python) timing out, not Snowflake server.
|
||||
|
||||
Let me check if there's a Python-side network timeout or client-side configuration.
|
||||
I see `network_timeout` being set. Let me check the timeout defaults.
|
||||
`connection_timeout: int = 30` ÔÇö that's the network_timeout which is being passed to `snowflake.connector.connect()` as `network_timeout=30`. This is the **network-level timeout** for Snowflake operations. According to the Snowflake Python connector docs, `network_timeout` is the timeout for socket-level operations, which includes query execution time. When a query takes longer than 30 seconds, the client kills it.
|
||||
|
||||
This is the root cause: `network_timeout=30` limits ALL query execution to 30 seconds, regardless of the `STATEMENT_TIMEOUT_IN_SECONDS` setting.
|
||||
|
||||
Now I have two options to fix this:
|
||||
1. **Increase `network_timeout`** ÔÇö but this would affect all queries
|
||||
2. **Pass a larger timeout to `execute_dict` calls in `get_patient_indication_groups`**
|
||||
|
||||
Actually wait ÔÇö the `network_timeout` parameter in the Snowflake connector is for network-level operations. Let me double-check. The old query (with `QUALIFY ROW_NUMBER() = 1`) worked within 30s for 500-patient batches, but the new `GROUP BY + COUNT(*)` doesn't.
|
||||
|
||||
The cleanest fix is to make the `get_patient_indication_groups()` function pass a longer timeout when calling `connector.execute_dict()`. But `execute_dict` doesn't have a way to change the network timeout ÔÇö it only changes the Snowflake-side `STATEMENT_TIMEOUT_IN_SECONDS`.
|
||||
|
||||
The real issue is the client-side `network_timeout=30`. This was fine for the old query but not for the new one. Let me think about the best approach:
|
||||
|
||||
1. **Optimize the query** to run within 30s ÔÇö this is the best long-term fix
|
||||
2. **Increase connection_timeout** ÔÇö easy but affects everything
|
||||
3. **Create a new connection with higher timeout** for GP lookups
|
||||
|
||||
Let me first try to understand if the query can be optimized. The main issue is the `GROUP BY + COUNT(*)` across potentially millions of GP records. Let me check the query structure and think about optimization.
|
||||
|
||||
Actually, looking at this more carefully, I think the most pragmatic approach is a combination:
|
||||
|
||||
1. **Optimize the query** ÔÇö we can use `COUNT(DISTINCT ...)` or add better filtering
|
||||
2. **Reduce batch size** from 500 to something smaller (say 100)
|
||||
3. **Increase the timeout** for this specific query path
|
||||
|
||||
But first, let me try running the query with just a few patients to confirm it works at all.
|
||||
Wrong table name. Let me check the actual Snowflake schema being used for the HCD data.
|
||||
OK. Let me now directly test the GP lookup query with a small number of patients. I'll pull from the same table used in the actual pipeline.
|
||||
Even with just 5 patients, the query times out at 30 seconds. This confirms the problem is the query execution time, not batch size. The `GROUP BY + COUNT(*)` version is fundamentally slower than the `QUALIFY ROW_NUMBER()` version.
|
||||
|
||||
The `PrimaryCareClinicalCoding` table is massive (national GP records). The join with the CTE + GROUP BY is too expensive. The original QUALIFY ROW_NUMBER() was fast because Snowflake could optimize it ÔÇö find the first match and stop.
|
||||
|
||||
Let me think about the optimization approach. The core problem is:
|
||||
1. Large CTE (148 rows  joined with SNOMED codes  potentially thousands of codes)
|
||||
2. Join with massive `PrimaryCareClinicalCoding` table
|
||||
3. GROUP BY forces a full scan
|
||||
|
||||
**Strategy**: Keep the efficient QUALIFY approach but return multiple rows. Instead of `ROW_NUMBER()`, use `DENSE_RANK()` or just return distinct Search_Terms with a `COUNT(*)` computed differently.
|
||||
|
||||
Actually, the best approach: split the query into two steps:
|
||||
1. First, find which patients have ANY match (fast with the existing join pattern)
|
||||
2. Then, for matched patients, compute the code_frequency
|
||||
|
||||
Or better: use a different approach to get code_frequency without GROUP BY on the massive table:
|
||||
|
||||
```sql
|
||||
-- Use QUALIFY to get distinct Search_Terms per patient (no aggregation)
|
||||
SELECT DISTINCT pc."PatientPseudonym", aic.Search_Term
|
||||
FROM PrimaryCareClinicalCoding pc
|
||||
JOIN AllIndicationCodes aic ON pc."SNOMEDCode" = aic.SNOMEDCode
|
||||
WHERE pc."PatientPseudonym" IN (...)
|
||||
```
|
||||
|
||||
This returns distinct patient-SearchTerm pairs without the expensive GROUP BY + COUNT(*). For the tiebreaker, we could:
|
||||
- Use a separate query to count codes (but that's also slow)
|
||||
- Use a window function instead of GROUP BY
|
||||
|
||||
Let me try the DISTINCT approach first ÔÇö it should be much faster.
|
||||
Even SELECT DISTINCT times out. The fundamental issue is the join between `PrimaryCareClinicalCoding` and the CTE-expanded SNOMED codes, filtered by patient and date. This is consistently timing out at ~30s even for 5 patients.
|
||||
|
||||
But wait ÔÇö the **old query** (with QUALIFY ROW_NUMBER()) worked! Let me verify that the old query still works, and check how long it takes.
|
||||
The old query takes 19.7s for 5 patients. That's already very close to the 30s timeout! With 500 patients, it would definitely take much longer. The fact that it worked before was likely because of caching or fewer patients at the time.
|
||||
|
||||
Now I understand the full picture:
|
||||
- Old query: ~20s for 5 patients (borderline, 500 patients would be ~30s+ with caching help)
|
||||
- New query (GROUP BY): even slower, consistently >30s
|
||||
- The `network_timeout=30` in the connector config kills both
|
||||
|
||||
The fix needs two parts:
|
||||
1. **Increase the network_timeout** to allow these queries to complete
|
||||
2. **Possibly optimize the query** for better performance
|
||||
|
||||
But the network_timeout is the immediate blocker. Let me fix that first. The cleanest approach is to pass a longer timeout when creating the connection for GP lookups. However, modifying the connector's network_timeout affects all queries.
|
||||
|
||||
Actually, looking at the Snowflake Python connector documentation, `network_timeout` controls the maximum time the client waits for the server response. To allow longer-running queries, we should increase this. The question is whether to increase it globally or per-connection.
|
||||
|
||||
The simplest approach: increase `connection_timeout` (network_timeout) in the config from 30 to 300 (or more). This is safe because the query-level `STATEMENT_TIMEOUT_IN_SECONDS` still controls how long Snowflake will run a query before killing it server-side.
|
||||
|
||||
Let me also check if we can pass timeout directly to `get_patient_indication_groups`  `execute_dict`.
|
||||
The most surgical fix: change `connection_timeout` from 30 to a higher value. But modifying a shared config file affects all Snowflake operations.
|
||||
|
||||
Actually, looking at `get_patient_indication_groups`, it calls `connector.execute_dict(query, tuple(params))` ÔÇö without passing a timeout parameter. But `execute_dict` DOES accept a `timeout` parameter which sets `STATEMENT_TIMEOUT_IN_SECONDS`. However, that only controls the server-side timeout, NOT the client-side `network_timeout`.
|
||||
|
||||
The real fix is to increase `network_timeout` when connecting. Let me look at whether `get_patient_indication_groups` can create its own connection with a higher timeout, or if we should just increase the default.
|
||||
|
||||
The pragmatic approach: increase `connection_timeout` from 30 to 600 (10 minutes). This is the **network-level** timeout (how long the client waits for any response from Snowflake). The query-level `query_timeout=300` still controls how long Snowflake will run a query. This is safe because:
|
||||
- Activity data fetch already takes ~10s and uses chunked queries (separate timeout mechanism)
|
||||
- GP lookup queries can take 20-30+ seconds per batch
|
||||
- The query_timeout (300s) is the actual safety net against runaway queries
|
||||
Now, there are two approaches to fix this:
|
||||
|
||||
**Option A**: Increase the global `connection_timeout` from 30 to 600 in the config. Simple but affects all queries.
|
||||
|
||||
**Option B**: Have `get_patient_indication_groups()` create its own connector with a higher network_timeout. More surgical but adds complexity.
|
||||
|
||||
I think Option A is the right call. The `connection_timeout=30` is just a network wait time, not a query execution limit. All other queries (like the activity data fetch) already work within 30s, so increasing it won't change their behavior. It just gives the GP lookup queries room to complete.
|
||||
|
||||
But I also need to consider query optimization. Even with a higher timeout, 20s per 5 patients ÔåÆ ~20-30 minutes for 36K patients in 500-patient batches (74 batches ├ù 20s = ~25 minutes). The old approach took ~9 minutes (according to CLAUDE.md). So the new GROUP BY query will be significantly slower.
|
||||
|
||||
Let me think about a better query strategy. Instead of `GROUP BY + COUNT(*)`, what about keeping the existing query pattern but removing the `QUALIFY ROW_NUMBER() = 1` to return all matches, and computing code_frequency client-side?
|
||||
|
||||
```sql
|
||||
-- Return ALL matches per patient (like the old query but without ROW_NUMBER() = 1 filter)
|
||||
SELECT DISTINCT
|
||||
pc."PatientPseudonym",
|
||||
aic.Search_Term,
|
||||
pc."SNOMEDCode"
|
||||
FROM PrimaryCareClinicalCoding pc
|
||||
JOIN AllIndicationCodes aic ON pc."SNOMEDCode" = aic.SNOMEDCode
|
||||
WHERE pc."PatientPseudonym" IN (...)
|
||||
AND pc."EventDateTime" >= ...
|
||||
```
|
||||
|
||||
Then compute `code_frequency` in Python: `df.groupby(['PatientPseudonym', 'Search_Term']).size()`.
|
||||
|
||||
Wait, that still returns all individual SNOMED codes which could be huge. And `SELECT DISTINCT` already timed out.
|
||||
|
||||
Let me try a different approach ÔÇö use `QUALIFY` with a higher rank instead of removing it entirely:
|
||||
|
||||
```sql
|
||||
-- Return multiple rows per patient but use window function to get count
|
||||
SELECT
|
||||
pc."PatientPseudonym",
|
||||
aic.Search_Term,
|
||||
COUNT(*) OVER (PARTITION BY pc."PatientPseudonym", aic.Search_Term) AS code_frequency
|
||||
FROM PrimaryCareClinicalCoding pc
|
||||
JOIN AllIndicationCodes aic ON pc."SNOMEDCode" = aic.SNOMEDCode
|
||||
WHERE ...
|
||||
QUALIFY ROW_NUMBER() OVER (PARTITION BY pc."PatientPseudonym", aic.Search_Term ORDER BY pc."EventDateTime" DESC) = 1
|
||||
```
|
||||
|
||||
This uses `ROW_NUMBER() = 1` but partitioned by `PatientPseudonym + Search_Term` (not just PatientPseudonym), so it returns one row per patient-SearchTerm pair. And the `COUNT(*) OVER(...)` window function gives us the code_frequency without a GROUP BY.
|
||||
|
||||
This should be similar in performance to the old query because it still uses the `QUALIFY ROW_NUMBER() = 1` optimization, just with a different partition.
|
||||
|
||||
Let me test this.
|
||||
Still timing out at 30s. The window function approach is just as slow. The fundamental problem is the `network_timeout=30` is too low for ANY variant of this query. The old query barely squeaked through at 19.7s for 5 patients.
|
||||
|
||||
The key insight: even the OLD working query takes ~20s per batch. When we ran the full pipeline in earlier iterations (according to CLAUDE.md, "~9 minutes for 37K patients"), that's ~74 batches × ~7s per batch with Snowflake query caching helping subsequent batches. But the first batch is always the slowest.
|
||||
|
||||
The fix is clear: **increase the connection_timeout** and then also increase it for `get_patient_indication_groups`. Let me also check if there's a way to make the query faster.
|
||||
|
||||
Actually, I realize the old query that took 19.7s was for the FIRST 5 random patients. In production, Snowflake caches the query plan and data after the first batch, so subsequent batches are much faster. This matches the "9 minutes for 37K patients" ÔÇö that's ~7-8 seconds per batch on average, with the first batch being much slower.
|
||||
|
||||
The fix is straightforward: increase `connection_timeout` to give the first batch enough time to complete. Let me do that.
|
||||
Now let me test again with the new timeout. Let me try the window function + QUALIFY approach first.
|
||||
Still timing out. The config change didn't take effect. The connector might be caching its configuration or reusing a connection. Let me check if the connector singleton is caching the old config.
|
||||
Let me check how the config is loaded and passed to the connector.
|
||||
The config is cached. Let me check what `load_snowflake_config` does ÔÇö it might read from snowflake.toml and override the defaults.
|
||||
I see ÔÇö line 185 has a hardcoded fallback: `connection_timeout=timeout_data.get("connection_timeout", 30)`. The TOML file overrides the dataclass default. Let me check the TOML file.
|
||||
Found it. Line 40: `connection_timeout = 30` in `config/snowflake.toml`. This is what gets loaded. The dataclass default I changed doesn't matter because the TOML overrides it. Let me update the TOML file.
|
||||
Also revert the dataclass default change since the TOML is the authoritative source.
|
||||
Wait ÔÇö the `load_snowflake_config` also has a hardcoded fallback on line 185: `timeout_data.get("connection_timeout", 30)`. I should update this fallback too for consistency, but actually let me leave it. The TOML value (600) will be used since the key exists. The fallback 30 only applies if the key is missing from TOML.
|
||||
|
||||
Actually, I should also update the fallback to match the new intent. Let me do that for consistency:
|
||||
Now let me test the query again with the updated timeout. The test will create a fresh connector since each `python -c` call starts fresh.
|
||||
The query now works with the 600s timeout. It took 39.6s for 5 patients ÔÇö significantly slower than the old QUALIFY query (19.7s). Let me now test with the 500-patient batch size to estimate total time.
|
||||
|
||||
Actually, let me first consider: at 39.6s per batch with batch_size=5, that's ~8s per patient. For 36K patients, that would be absurd. But the first batch is always the slowest (cold cache). Let me test with 500 patients to see if Snowflake's caching kicks in.
|
||||
Excellent. The 500-patient batch takes 38.4s ÔÇö very similar to the 5-patient batch (39.6s). This confirms the bottleneck is query compilation/planning, not the data volume. Snowflake's query plan for the CTE+JOIN is the expensive part, and it's roughly constant regardless of batch size.
|
||||
|
||||
Key data:
|
||||
- 500 patients  38.4s, 5517 rows, 488 matched (97.6% match rate)
|
||||
- Avg 11.3 indications per patient (this includes non-drug-related diagnoses like "sepsis", "influenza")
|
||||
- 115 unique Search_Terms found
|
||||
|
||||
For 36K patients at 500/batch = 72 batches × ~38s = ~46 minutes. That's significantly longer than the old approach (9 minutes). But subsequent batches might be faster due to Snowflake query caching.
|
||||
|
||||
Let me test a second batch to see if caching helps.
|
||||
No caching benefit ÔÇö still ~40s per batch. This means 72 batches ├ù 40s = ~48 minutes just for the GP lookup. That's too slow. Let me think about optimization.
|
||||
|
||||
The core problem: each batch re-executes the massive CTE (148 cluster mappings  SNOMED codes expansion) and joins with the huge `PrimaryCareClinicalCoding` table. The CTE is identical across all batches  only the patient list changes.
|
||||
|
||||
**Optimization ideas:**
|
||||
1. **Larger batch size** ÔÇö If 500 patients takes 40s and 5 patients also takes 40s, then the batch size doesn't matter much. Try 2000 or 5000 patients per batch to reduce the number of CTE compilations.
|
||||
2. **Skip code_frequency** ÔÇö Use DISTINCT instead of GROUP BY + COUNT(*), compute frequency client-side later if needed at all
|
||||
3. **Pre-create temp table** for the CTE results, then query against that
|
||||
|
||||
Let me try option 1 first ÔÇö increase batch size to 5000.
|
||||
Huge improvement. 5000 patients takes 41.3s ÔÇö almost the same as 500 patients. The overhead is in the CTE compilation, not the data volume. With 5000-patient batches:
|
||||
- 36K patients / 5000 per batch = ~8 batches
|
||||
- 8 × 41s = ~5.5 minutes for GP lookups
|
||||
|
||||
That's reasonable and much better than the 48 minutes with 500-patient batches. Let me try an even larger batch.
|
||||
10K patients takes ~43s. The time is nearly constant regardless of batch size (40-43s), confirming the overhead is CTE compilation. With 10K-patient batches:
|
||||
- ~42K patients / 10000 = ~5 batches × 43s = ~3.5 minutes
|
||||
|
||||
That's excellent. Let me now update the batch size in `get_patient_indication_groups()` from 500 to 5000. I'll use 5000 rather than 10000 to stay conservative (10K placeholders in an IN clause is pushing it).
|
||||
|
||||
Actually wait ÔÇö the caller in `refresh_pathways.py` passes `batch_size=500`. Let me check.
|
||||
I need to change the batch_size in the caller. Let me update both the default in the function and the call site.
|
||||
There are two functions with `batch_size: int = 500`. Let me update the specific one.
|
||||
Now update the caller in refresh_pathways.py:
|
||||
Let me verify both files compile correctly.
|
||||
Now let me run the full dry run test with the fixes applied.
|
||||
The dry run is running in the background. It will take a while ÔÇö Snowflake fetch (~10s), data transformations (~6 min), GP lookup (~5 min with new batch sizes), pathway processing (~1 min). Let me check on it periodically.
|
||||
Data transformations are running. Let me wait for them to complete and check the GP lookup phase.
|
||||
The first batch completed successfully: **Batch 1: 54,300 rows in 48.72s**. The GP lookup is now working with the 5000-patient batches and 600s network timeout. Batch 2 is running. 8 batches total.
|
||||
|
||||
Let me wait for the full pipeline to complete.
|
||||
The dry run completed successfully! Let me read the full output to get all the key metrics.
|
||||
Let me also see the tiebreaker and top indications stats.
|
||||
Excellent. Here's the full picture of the dry run results:
|
||||
|
||||
**GP Lookup Results:**
|
||||
- 36,628 patients queried  33,642 with GP match (91.8%)
|
||||
- 8 batches of 5000 patients, ~40-48s each
|
||||
- 537,794 total patient-indication matches (avg 16 per patient ÔÇö includes non-drug-related like sepsis, influenza)
|
||||
- Top GP indications: sepsis (32K), drug misuse (31K), influenza (28K)
|
||||
|
||||
**Drug-Aware Matching Results:**
|
||||
- 50,797 UPID-Drug pairs processed
|
||||
- **25,059 matched (49.3%)** ÔÇö drug has a DimSearchTerm mapping AND patient has GP dx for that condition
|
||||
- **25,738 fallback (50.7%)** ÔÇö no match, falls back to directory
|
||||
- **15,238 tiebreaker used** ÔÇö drug matched multiple indications, picked by highest code_frequency
|
||||
- **42,072 unique modified UPIDs** (up from original ~37K unique patients)
|
||||
- Top assigned indications: "Undefined (no GP dx)" (8,110), macular degeneration (5,256), diabetes (3,841), "OPHTHALMOLOGY (no GP dx)" (3,040), rheumatoid arthritis (2,216)
|
||||
|
||||
**Pathway Node Counts:**
|
||||
| Date Filter | Nodes |
|
||||
|---|---|
|
||||
| all_6mo | 438 |
|
||||
| all_12mo | 484 |
|
||||
| 1yr_6mo | 181 |
|
||||
| 1yr_12mo | 199 |
|
||||
| 2yr_6mo | 257 |
|
||||
| 2yr_12mo | 287 |
|
||||
| **Total** | **1,846** |
|
||||
|
||||
This is comparable to the old approach (~300 per date filter for the wider filters). The 49.3% match rate seems reasonable ÔÇö drugs without a DimSearchTerm mapping (or where the patient has no GP coding for the drug's indication) fall back to directory labels.
|
||||
|
||||
The dry run is a success. Let me now mark Task 3.2 as complete, commit the changes, and update progress.
|
||||
Let me update the IMPLEMENTATION_PLAN.md to mark Task 3.2 complete.
|
||||
Now update progress.txt with the iteration log.
|
||||
There are 4 occurrences of "### Blocked items:\n- None" in the file. I need to be more specific. Let me append to the end of the file instead.
|
||||
Now let me add a guardrail for the timeout issue since it's a failure pattern future iterations should know about.
|
||||
Now let me commit all the changes.
|
||||
There are many changed files from prior uncommitted work (DESIGN_SYSTEM.md, RALPH_PROMPT.md, etc.). I should only commit the files I changed for Task 3.2. Let me stage just the relevant files.
|
||||
Now update progress.txt with the commit hash.
|
||||
Task 3.2 is complete. Let me verify the final state.
|
||||
## Summary ÔÇö Iteration 6 Complete
|
||||
|
||||
**Task 3.2: Test with dry run** ÔÇö COMPLETE
|
||||
|
||||
### Key findings and fixes:
|
||||
1. **Root cause**: GP lookup queries timing out at 30s due to `connection_timeout=30` in snowflake.toml (controls Python client `network_timeout`, not server-side timeout)
|
||||
2. **Fix 1**: Increased `connection_timeout` from 30  600 seconds
|
||||
3. **Fix 2**: Increased GP lookup batch_size from 500  5000 (query time is ~40s regardless of batch size  bottleneck is CTE compilation, not data volume)
|
||||
4. **Dry run successful**:
|
||||
- 91.8% GP match rate
|
||||
- 49.3% drug-indication match rate, 50.7% fallback to directory
|
||||
- 15,238 tiebreaker decisions made
|
||||
- 42,072 unique modified UPIDs
|
||||
- 1,846 total pathway nodes across 6 date filters
|
||||
|
||||
### Next: Task 4.1 (Full refresh with both chart types)
|
||||
## Summary ÔÇö Iteration 6 Complete
|
||||
|
||||
**Task 3.2: Test with dry run** ÔÇö COMPLETE
|
||||
|
||||
### Key findings and fixes:
|
||||
1. **Root cause**: GP lookup queries timing out at 30s due to `connection_timeout=30` in snowflake.toml (controls Python client `network_timeout`, not server-side timeout)
|
||||
2. **Fix 1**: Increased `connection_timeout` from 30  600 seconds
|
||||
3. **Fix 2**: Increased GP lookup batch_size from 500  5000 (query time is ~40s regardless of batch size  bottleneck is CTE compilation, not data volume)
|
||||
4. **Dry run successful**:
|
||||
- 91.8% GP match rate
|
||||
- 49.3% drug-indication match rate, 50.7% fallback to directory
|
||||
- 15,238 tiebreaker decisions made
|
||||
- 42,072 unique modified UPIDs
|
||||
- 1,846 total pathway nodes across 6 date filters
|
||||
|
||||
### Next: Task 4.1 (Full refresh with both chart types)
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,132 @@
|
||||
|
||||
|
||||
I'll start by reading the required files in order to understand the current state of the project.
|
||||
Good. I've read progress.txt, IMPLEMENTATION_PLAN.md, and guardrails.md. Now let me check recent git history.
|
||||
Now I have the full picture. Let me assess the state:
|
||||
|
||||
**Completed**: Phase 1 (1.1, 1.2, 1.3), Phase 2 (2.1, 2.2), Phase 3 (3.1, 3.2)
|
||||
**Remaining**: Phase 4 ÔÇö Tasks 4.1, 4.2, 4.3
|
||||
|
||||
The previous iteration (6) recommended working on **Task 4.1: Full refresh with both chart types**. This requires running `python -m cli.refresh_pathways --chart-type all` (a live Snowflake refresh that takes ~15 minutes).
|
||||
|
||||
**Why Task 4.1 next**: It's the next logical step ÔÇö dry run passed, now we do the actual insert. Tasks 4.2 and 4.3 depend on 4.1 completing (they validate the results).
|
||||
|
||||
Let me mark Task 4.1 as in-progress in the implementation plan, then run the full refresh.
|
||||
Running the full refresh now. This will fetch from Snowflake, transform data, run GP lookups, and insert both directory and indication pathway nodes into SQLite. Expected time: ~15 minutes.
|
||||
The refresh is running in the background. Let me check the output so far.
|
||||
The refresh is progressing well. Directory charts completed (1,101 nodes across 6 filters), and now indication charts are being processed. GP lookup completed successfully (91.8% match rate, 42,072 modified UPIDs). Indication chart processing is underway ÔÇö I can see all_6mo (438 nodes) and all_12mo starting. Let me wait for it to finish.
|
||||
Full refresh completed successfully. Let me summarize the results:
|
||||
|
||||
**Full Refresh Results (Task 4.1):**
|
||||
- **Total time**: 738.4 seconds (~12 minutes)
|
||||
- **Total nodes inserted**: 2,947 (cleared 3,633 old nodes first)
|
||||
- **Directory charts**: 1,101 nodes across 6 date filters
|
||||
- **Indication charts**: 1,846 nodes across 6 date filters
|
||||
- **GP lookup**: 36,628 patients, 33,642 matched (91.8%), 8 batches in ~30s total
|
||||
- **Drug-indication matching**: 50,797 UPID-Drug pairs  25,059 matched (49.3%), 15,238 tiebreakers, 25,738 fallback
|
||||
- **Modified UPIDs**: 42,072 (up from 36,628 original patients)
|
||||
|
||||
Both chart types generated data successfully. Let me update the todo list and proceed to Task 4.2 (validate indication chart correctness).
|
||||
Now validating the indication chart data in SQLite. I'll run several queries to verify correctness ÔÇö checking drug groupings, separate pathways for different indications, and fallback behavior.
|
||||
I need you to validate the data in a SQLite database at `C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign\data\pathways.db`.
|
||||
|
||||
Run these validation queries using Bash with `python -c "..."` or `sqlite3` to check the pathway_nodes table. The working directory is `C:\Users\charlwoodand\Ralph local\Tasks\UI Redesign`.
|
||||
|
||||
**Context**: This is a pre-computed pathway hierarchy for NHS patient treatment data. There are two chart types: "directory" and "indication". We just ran a full refresh and need to validate the indication charts specifically.
|
||||
|
||||
The `ids` column contains a hierarchy like: `Trust - Indication - Drug - Pathway`, separated by " - ".
|
||||
For indication charts, level 2 = indication/search_term (e.g., "rheumatoid arthritis", "asthma").
|
||||
Modified UPIDs embed the indication: `{UPID}|{search_term}`.
|
||||
|
||||
**Run these validation queries** (each as a separate `sqlite3` command or python script):
|
||||
|
||||
1. **Chart type counts**: Count nodes per chart_type to confirm both exist:
|
||||
```sql
|
||||
SELECT chart_type, COUNT(*) FROM pathway_nodes GROUP BY chart_type;
|
||||
```
|
||||
|
||||
2. **Indication node counts by date filter**: For indication charts, count nodes per date_filter_id:
|
||||
```sql
|
||||
SELECT date_filter_id, COUNT(*) FROM pathway_nodes WHERE chart_type='indication' GROUP BY date_filter_id;
|
||||
```
|
||||
|
||||
3. **Level distribution for indication charts**: Check hierarchy levels are correct (0=Root, 1=Trust, 2=Indication, 3=Drug, 4+=Pathway):
|
||||
```sql
|
||||
SELECT level, COUNT(*) FROM pathway_nodes WHERE chart_type='indication' GROUP BY level ORDER BY level;
|
||||
```
|
||||
|
||||
4. **Top indications by patient count** (level 2 = indication): Show the labels and patient counts for the biggest indication groups (default date filter 'all_6mo'):
|
||||
```sql
|
||||
SELECT labels, value FROM pathway_nodes WHERE chart_type='indication' AND date_filter_id='all_6mo' AND level=2 ORDER BY CAST(value AS INTEGER) DESC LIMIT 15;
|
||||
```
|
||||
|
||||
5. **Drugs under "rheumatoid arthritis"**: Check that drugs under RA are RA drugs. For date_filter 'all_6mo', find level 3 nodes whose parent contains "rheumatoid arthritis":
|
||||
```sql
|
||||
SELECT labels, value FROM pathway_nodes WHERE chart_type='indication' AND date_filter_id='all_6mo' AND level=3 AND ids LIKE '%rheumatoid arthritis%' ORDER BY CAST(value AS INTEGER) DESC LIMIT 10;
|
||||
```
|
||||
|
||||
6. **Drugs under "asthma"**: Similarly check asthma drugs:
|
||||
```sql
|
||||
SELECT labels, value FROM pathway_nodes WHERE chart_type='indication' AND date_filter_id='all_6mo' AND level=3 AND ids LIKE '%asthma%' ORDER BY CAST(value AS INTEGER) DESC LIMIT 10;
|
||||
```
|
||||
|
||||
7. **Fallback nodes** (no GP dx): Count fallback indication labels:
|
||||
```sql
|
||||
SELECT labels, value FROM pathway_nodes WHERE chart_type='indication' AND date_filter_id='all_6mo' AND level=2 AND labels LIKE '%(no GP dx)%' ORDER BY CAST(value AS INTEGER) DESC LIMIT 10;
|
||||
```
|
||||
|
||||
8. **Directory charts unchanged**: Verify directory chart node count is reasonable and unaffected:
|
||||
```sql
|
||||
SELECT date_filter_id, COUNT(*) FROM pathway_nodes WHERE chart_type='directory' GROUP BY date_filter_id;
|
||||
```
|
||||
|
||||
Run all 8 queries and report the results. I need to confirm:
|
||||
- Both chart types have data
|
||||
- Indication chart has correct hierarchy levels
|
||||
- RA drugs under RA indication look correct (should include ADALIMUMAB, RITUXIMAB, etc.)
|
||||
- Asthma drugs under asthma look correct (should include OMALIZUMAB, MEPOLIZUMAB, etc.)
|
||||
- Fallback nodes exist for unmatched drugs
|
||||
- Directory charts are unchanged
|
||||
All 8 validation queries passed. The indication chart data looks correct:
|
||||
|
||||
- RA drugs (ADALIMUMAB, RITUXIMAB, BARICITINIB, etc.) correctly grouped under "rheumatoid arthritis"
|
||||
- Asthma drugs (DUPILUMAB, OMALIZUMAB) correctly grouped under "asthma"
|
||||
- Fallback nodes properly labeled (e.g., "RHEUMATOLOGY (no GP dx)")
|
||||
- Directory charts unchanged
|
||||
|
||||
Now let me validate the Reflex UI compilation (Task 4.3).
|
||||
Reflex compiles successfully. Now let me mark all the Phase 4 tasks as complete in the implementation plan and update progress.txt.
|
||||
Also mark the last Task 1.1 subtask as verified:
|
||||
Now update progress.txt with this iteration's results.
|
||||
Need more context to uniquely identify the last occurrence. Let me target it specifically.
|
||||
Now append the new iteration entry at the end of the file.
|
||||
Now let me commit the changes.
|
||||
I see there are many changes beyond just my iteration's work (DESIGN_SYSTEM.md, RALPH_PROMPT.md, ralph.ps1, uv.lock, etc. from previous uncommitted changes). I should only commit the files relevant to this iteration's task: IMPLEMENTATION_PLAN.md and progress.txt. The other changes are pre-existing and not part of this task.
|
||||
Now let me verify all tasks are marked complete in IMPLEMENTATION_PLAN.md.
|
||||
No incomplete tasks remain. All tasks are `[x]`. Let me do the final completion check ÔÇö verify all completion criteria are satisfied.
|
||||
Wait, that output is confusing ÔÇö let me re-check.
|
||||
All 47 items marked complete, zero incomplete. All completion criteria are satisfied:
|
||||
|
||||
- App compiles without errors (reflex compile succeeded in 16.6s)
|
||||
- Both chart types generate pathway data (directory: 1,101, indication: 1,846)
|
||||
- Indication charts show drug-specific indication matching (49.3% match rate)
|
||||
- Drugs under same indication grouped together (validated via SQLite queries)
|
||||
- Drugs under different indications create separate pathways (42,072 modified UPIDs > 36,628 patients)
|
||||
- Fallback works for unmatched drugs (labeled with directorate "(no GP dx)")
|
||||
- Full refresh completes successfully (2,947 records in 738.4s)
|
||||
- Existing directory charts unaffected (1,101 nodes, same distribution)
|
||||
|
||||
<promise>COMPLETE</promise>
|
||||
All 47 items marked complete, zero incomplete. All completion criteria are satisfied:
|
||||
|
||||
- App compiles without errors (reflex compile succeeded in 16.6s)
|
||||
- Both chart types generate pathway data (directory: 1,101, indication: 1,846)
|
||||
- Indication charts show drug-specific indication matching (49.3% match rate)
|
||||
- Drugs under same indication grouped together (validated via SQLite queries)
|
||||
- Drugs under different indications create separate pathways (42,072 modified UPIDs > 36,628 patients)
|
||||
- Fallback works for unmatched drugs (labeled with directorate "(no GP dx)")
|
||||
- Full refresh completes successfully (2,947 records in 738.4s)
|
||||
- Existing directory charts unaffected (1,101 nodes, same distribution)
|
||||
|
||||
<promise>COMPLETE</promise>
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,231 @@
|
||||
Search_Term,PrimaryDirectorate,AllDirectorates
|
||||
acute coronary syndrome,CARDIOLOGY,CARDIOLOGY
|
||||
acute coronary syndromes,CARDIOLOGY,CARDIOLOGY
|
||||
acute lymphoblastic leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|PAEDIATRICS
|
||||
acute myeloid leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
acute promyelocytic leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
advanced breast cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|BREAST SURGERY|CLINICAL ONCOLOGY
|
||||
allergic asthma,THORACIC MEDICINE,THORACIC MEDICINE|CLINICAL IMMUNOLOGY
|
||||
allergic rhinitis,ENT,ENT|CLINICAL IMMUNOLOGY
|
||||
alzheimer's disease,NEUROLOGY,NEUROLOGY|GERIATRIC MEDICINE|MENTAL HEALTH
|
||||
amyloidosis,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|CARDIOLOGY|NEPHROLOGY
|
||||
anaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|GENERAL MEDICINE
|
||||
anaplastic large cell lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
angioedema,CLINICAL IMMUNOLOGY,CLINICAL IMMUNOLOGY|ACCIDENT & EMERGENCY
|
||||
ankylosing spondylitis,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
apixaban,CARDIOLOGY,CARDIOLOGY|CLINICAL HAEMATOLOGY
|
||||
aplastic anaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
arthritis,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
asthma,THORACIC MEDICINE,THORACIC MEDICINE|PAEDIATRICS
|
||||
atopic dermatitis,DERMATOLOGY,DERMATOLOGY|PAEDIATRICS|CLINICAL IMMUNOLOGY
|
||||
atrial fibrillation,CARDIOLOGY,CARDIOLOGY
|
||||
attention deficit hyperactivity disorder,MENTAL HEALTH,MENTAL HEALTH|PAEDIATRICS
|
||||
attention-deficit hyperactivity disorder,MENTAL HEALTH,MENTAL HEALTH|PAEDIATRICS
|
||||
axial spondyloarthritis,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
basal cell carcinoma,DERMATOLOGY,DERMATOLOGY|PLASTIC SURGERY|MEDICAL ONCOLOGY
|
||||
beta-thalassaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|PAEDIATRICS
|
||||
biliary cholangitis,GASTROENTEROLOGY,GASTROENTEROLOGY
|
||||
bipolar disorder,MENTAL HEALTH,MENTAL HEALTH
|
||||
bladder,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
braf,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|CLINICAL ONCOLOGY
|
||||
brca,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GYNAECOLOGICAL ONCOLOGY|BREAST SURGERY
|
||||
breast cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|BREAST SURGERY|CLINICAL ONCOLOGY
|
||||
cardiomyopathy,CARDIOLOGY,CARDIOLOGY
|
||||
cardiovascular disease,CARDIOLOGY,CARDIOLOGY|VASCULAR SURGERY
|
||||
cervical cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GYNAECOLOGICAL ONCOLOGY|CLINICAL ONCOLOGY
|
||||
cholangiocarcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GASTROENTEROLOGY|CLINICAL ONCOLOGY
|
||||
choroidal neovascularisation,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY
|
||||
chronic hepatitis b,GASTROENTEROLOGY,GASTROENTEROLOGY|INFECTIOUS DISEASES
|
||||
chronic kidney disease,NEPHROLOGY,NEPHROLOGY
|
||||
chronic liver disease,GASTROENTEROLOGY,GASTROENTEROLOGY
|
||||
chronic lymphocytic leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
chronic myeloid leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
chronic obstructive pulmonary disease,THORACIC MEDICINE,THORACIC MEDICINE
|
||||
colon cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|COLORECTAL SURGERY|CLINICAL ONCOLOGY
|
||||
colorectal cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|COLORECTAL SURGERY|CLINICAL ONCOLOGY
|
||||
constipation,GASTROENTEROLOGY,GASTROENTEROLOGY|GENERAL MEDICINE
|
||||
coronary syndrome,CARDIOLOGY,CARDIOLOGY
|
||||
covid,INFECTIOUS DISEASES,INFECTIOUS DISEASES|THORACIC MEDICINE
|
||||
covid-19,INFECTIOUS DISEASES,INFECTIOUS DISEASES|THORACIC MEDICINE
|
||||
crohn's disease,GASTROENTEROLOGY,GASTROENTEROLOGY|PAEDIATRIC GASTROENTEROLOGY|COLORECTAL SURGERY
|
||||
cutaneous t-cell lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|DERMATOLOGY
|
||||
cystic fibrosis,THORACIC MEDICINE,THORACIC MEDICINE|PAEDIATRICS|GASTROENTEROLOGY
|
||||
cytomegalovirus,INFECTIOUS DISEASES,INFECTIOUS DISEASES|TRANSPLANTATION SURGERY
|
||||
deep vein thrombosis,VASCULAR SURGERY,VASCULAR SURGERY|CLINICAL HAEMATOLOGY
|
||||
depression,MENTAL HEALTH,MENTAL HEALTH
|
||||
depressive episode,MENTAL HEALTH,MENTAL HEALTH
|
||||
diabetes,DIABETIC MEDICINE,DIABETIC MEDICINE|ENDOCRINOLOGY
|
||||
diabetic macular,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY
|
||||
diabetic macular oedema,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY
|
||||
diabetic retinopathy,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY|DIABETIC MEDICINE
|
||||
diffuse large b-cell lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
dravet syndrome,NEUROLOGY,NEUROLOGY|PAEDIATRICS
|
||||
drug misuse,MENTAL HEALTH,MENTAL HEALTH|ADDICTION MEDICINE
|
||||
dry eye,OPHTHALMOLOGY,OPHTHALMOLOGY
|
||||
dupuytren's contracture,TRAUMA & ORTHOPAEDICS,TRAUMA & ORTHOPAEDICS|PLASTIC SURGERY
|
||||
dyslipidaemia,CARDIOLOGY,CARDIOLOGY|ENDOCRINOLOGY
|
||||
dyspepsia,GASTROENTEROLOGY,GASTROENTEROLOGY|GENERAL MEDICINE
|
||||
eczema,DERMATOLOGY,DERMATOLOGY|PAEDIATRICS
|
||||
endometrial cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GYNAECOLOGICAL ONCOLOGY|CLINICAL ONCOLOGY
|
||||
epilepsy,NEUROLOGY,NEUROLOGY|PAEDIATRICS
|
||||
fallopian tube,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GYNAECOLOGICAL ONCOLOGY|CLINICAL ONCOLOGY
|
||||
fibroids,GYNAECOLOGY,GYNAECOLOGY
|
||||
follicular lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
fragility fracture,RHEUMATOLOGY,RHEUMATOLOGY|TRAUMA & ORTHOPAEDICS|GERIATRIC MEDICINE
|
||||
gastric cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
gastro-oesophageal,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
gastro-oesophageal junction,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
gastrointestinal stromal tumour,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
gastrointestinal stromal tumours,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
giant cell arteritis,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
glioma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|NEUROSURGERY|CLINICAL ONCOLOGY
|
||||
gout,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
graft versus host disease,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|TRANSPLANTATION SURGERY
|
||||
granulomatosis with polyangiitis,RHEUMATOLOGY,RHEUMATOLOGY|THORACIC MEDICINE|NEPHROLOGY
|
||||
growth failure,ENDOCRINOLOGY,ENDOCRINOLOGY|PAEDIATRICS
|
||||
growth hormone deficiency,ENDOCRINOLOGY,ENDOCRINOLOGY|PAEDIATRICS
|
||||
haemoglobinuria,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
hand eczema,DERMATOLOGY,DERMATOLOGY
|
||||
head and neck,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|ENT|CLINICAL ONCOLOGY
|
||||
heart failure,CARDIOLOGY,CARDIOLOGY
|
||||
hepatic encephalopathy,GASTROENTEROLOGY,GASTROENTEROLOGY
|
||||
hepatitis b,GASTROENTEROLOGY,GASTROENTEROLOGY|INFECTIOUS DISEASES
|
||||
hepatitis c,GASTROENTEROLOGY,GASTROENTEROLOGY|INFECTIOUS DISEASES
|
||||
hepatocellular carcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GASTROENTEROLOGY|CLINICAL ONCOLOGY
|
||||
her2,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|BREAST SURGERY|CLINICAL ONCOLOGY
|
||||
her2-positive,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|BREAST SURGERY|CLINICAL ONCOLOGY
|
||||
hereditary angioedema,CLINICAL IMMUNOLOGY,CLINICAL IMMUNOLOGY
|
||||
hidradenitis suppurativa,DERMATOLOGY,DERMATOLOGY
|
||||
hiv,INFECTIOUS DISEASES,INFECTIOUS DISEASES
|
||||
hodgkin lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
hormone receptor,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|BREAST SURGERY|CLINICAL ONCOLOGY
|
||||
hypercholesterolaemia,CARDIOLOGY,CARDIOLOGY|ENDOCRINOLOGY|CHEMICAL PATHOLOGY
|
||||
hyperparathyroidism,ENDOCRINOLOGY,ENDOCRINOLOGY
|
||||
hyperuricaemia,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
immune thrombocytopenia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
influenza,INFECTIOUS DISEASES,INFECTIOUS DISEASES|GENERAL MEDICINE
|
||||
insomnia,NEUROLOGY,NEUROLOGY|MENTAL HEALTH
|
||||
interstitial lung disease,THORACIC MEDICINE,THORACIC MEDICINE
|
||||
irritable bowel syndrome,GASTROENTEROLOGY,GASTROENTEROLOGY
|
||||
ischaemic stroke,STROKE MEDICINE,STROKE MEDICINE|NEUROLOGY
|
||||
juvenile idiopathic arthritis,RHEUMATOLOGY,RHEUMATOLOGY|PAEDIATRICS
|
||||
keratitis,OPHTHALMOLOGY,OPHTHALMOLOGY
|
||||
kidney disease,NEPHROLOGY,NEPHROLOGY
|
||||
kidney transplant,NEPHROLOGY,NEPHROLOGY|TRANSPLANTATION SURGERY
|
||||
large b-cell lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
limbal stem cell deficiency,OPHTHALMOLOGY,OPHTHALMOLOGY
|
||||
liver disease,GASTROENTEROLOGY,GASTROENTEROLOGY
|
||||
lung cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|THORACIC MEDICINE|CLINICAL ONCOLOGY
|
||||
lymphoblastic leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|PAEDIATRICS
|
||||
lymphocytic leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
macular degeneration,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY
|
||||
macular oedema,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY
|
||||
major depressive episodes,MENTAL HEALTH,MENTAL HEALTH
|
||||
malignant melanoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|DERMATOLOGY|CLINICAL ONCOLOGY
|
||||
malignant pleural mesothelioma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|THORACIC MEDICINE|CLINICAL ONCOLOGY
|
||||
manic episode,MENTAL HEALTH,MENTAL HEALTH
|
||||
mantle cell lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
mastocytosis,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|CLINICAL IMMUNOLOGY
|
||||
melanoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|DERMATOLOGY|CLINICAL ONCOLOGY
|
||||
merkel cell,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|DERMATOLOGY|CLINICAL ONCOLOGY
|
||||
merkel cell carcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|DERMATOLOGY|CLINICAL ONCOLOGY
|
||||
mesothelioma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|THORACIC MEDICINE|CLINICAL ONCOLOGY
|
||||
metastatic colorectal cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|COLORECTAL SURGERY|CLINICAL ONCOLOGY
|
||||
migraine,NEUROLOGY,NEUROLOGY
|
||||
motor neurone disease,NEUROLOGY,NEUROLOGY|REHABILITATION|PALLIATIVE CARE
|
||||
multiple myeloma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
multiple sclerosis,NEUROLOGY,NEUROLOGY|REHABILITATION
|
||||
myelodysplastic,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
myelodysplastic syndromes,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
myelofibrosis,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
myeloid leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
myocardial infarction,CARDIOLOGY,CARDIOLOGY
|
||||
myotonia,NEUROLOGY,NEUROLOGY
|
||||
narcolepsy,NEUROLOGY,NEUROLOGY
|
||||
nasal polyps,ENT,ENT|THORACIC MEDICINE|CLINICAL IMMUNOLOGY
|
||||
neuroblastoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|PAEDIATRICS|CLINICAL ONCOLOGY
|
||||
neuroendocrine tumour,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|ENDOCRINOLOGY|CLINICAL ONCOLOGY
|
||||
non-small cell lung cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|THORACIC MEDICINE|CLINICAL ONCOLOGY
|
||||
non-small-cell lung cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|THORACIC MEDICINE|CLINICAL ONCOLOGY
|
||||
obesity,ENDOCRINOLOGY,ENDOCRINOLOGY|DIABETIC MEDICINE|GENERAL MEDICINE
|
||||
oesophageal cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
osteoarthritis,RHEUMATOLOGY,RHEUMATOLOGY|TRAUMA & ORTHOPAEDICS|GERIATRIC MEDICINE
|
||||
osteoporosis,RHEUMATOLOGY,RHEUMATOLOGY|ENDOCRINOLOGY|GERIATRIC MEDICINE
|
||||
osteosarcoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|TRAUMA & ORTHOPAEDICS|CLINICAL ONCOLOGY
|
||||
ovarian cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|GYNAECOLOGICAL ONCOLOGY|CLINICAL ONCOLOGY
|
||||
overweight,ENDOCRINOLOGY,ENDOCRINOLOGY|DIABETIC MEDICINE
|
||||
pancreatic cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UPPER GASTROINTESTINAL SURGERY|CLINICAL ONCOLOGY
|
||||
pancreatic neuroendocrine,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|ENDOCRINOLOGY|CLINICAL ONCOLOGY
|
||||
paroxysmal nocturnal haemoglobinuria,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
peanut allergy,CLINICAL IMMUNOLOGY,CLINICAL IMMUNOLOGY|PAEDIATRICS
|
||||
perianal fistula,GASTROENTEROLOGY,GASTROENTEROLOGY|COLORECTAL SURGERY
|
||||
peripheral arterial disease,VASCULAR SURGERY,VASCULAR SURGERY|CARDIOLOGY
|
||||
plaque psoriasis,DERMATOLOGY,DERMATOLOGY
|
||||
polycystic kidney,NEPHROLOGY,NEPHROLOGY
|
||||
polycystic kidney disease,NEPHROLOGY,NEPHROLOGY
|
||||
polycythaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
polycythaemia vera,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
pouchitis,GASTROENTEROLOGY,GASTROENTEROLOGY|COLORECTAL SURGERY
|
||||
pregnancy,OBSTETRICS,OBSTETRICS
|
||||
primary biliary cholangitis,GASTROENTEROLOGY,GASTROENTEROLOGY
|
||||
primary hypercholesterolaemia,CARDIOLOGY,CARDIOLOGY|ENDOCRINOLOGY|CHEMICAL PATHOLOGY
|
||||
promyelocytic leukaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
prostate cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
psoriasis,DERMATOLOGY,DERMATOLOGY
|
||||
psoriatic arthritis,RHEUMATOLOGY,RHEUMATOLOGY|DERMATOLOGY
|
||||
pulmonary embolism,THORACIC MEDICINE,THORACIC MEDICINE|CARDIOLOGY|CLINICAL HAEMATOLOGY
|
||||
pulmonary fibrosis,THORACIC MEDICINE,THORACIC MEDICINE
|
||||
relapsing multiple sclerosis,NEUROLOGY,NEUROLOGY|REHABILITATION
|
||||
renal cell,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
renal cell carcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
renal transplantation,NEPHROLOGY,NEPHROLOGY|TRANSPLANTATION SURGERY
|
||||
retinal vein occlusion,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY
|
||||
rheumatoid arthritis,RHEUMATOLOGY,RHEUMATOLOGY|CLINICAL IMMUNOLOGY|GERIATRIC MEDICINE
|
||||
rhinosinusitis with nasal polyps,ENT,ENT|THORACIC MEDICINE|CLINICAL IMMUNOLOGY
|
||||
rivaroxaban,CARDIOLOGY,CARDIOLOGY|CLINICAL HAEMATOLOGY
|
||||
schizophrenia,MENTAL HEALTH,MENTAL HEALTH
|
||||
seizures,NEUROLOGY,NEUROLOGY|PAEDIATRICS
|
||||
sepsis,INFECTIOUS DISEASES,INFECTIOUS DISEASES|CRITICAL CARE MEDICINE
|
||||
severe persistent allergic asthma,THORACIC MEDICINE,THORACIC MEDICINE|CLINICAL IMMUNOLOGY
|
||||
short bowel syndrome,GASTROENTEROLOGY,GASTROENTEROLOGY|COLORECTAL SURGERY
|
||||
sickle cell,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|PAEDIATRICS
|
||||
sickle cell disease,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|PAEDIATRICS
|
||||
sleep apnoea,THORACIC MEDICINE,THORACIC MEDICINE|ENT
|
||||
smoking cessation,THORACIC MEDICINE,THORACIC MEDICINE|GENERAL MEDICINE
|
||||
soft tissue sarcoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|CLINICAL ONCOLOGY
|
||||
spinal muscular atrophy,NEUROLOGY,NEUROLOGY|PAEDIATRICS
|
||||
splenomegaly,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|GASTROENTEROLOGY
|
||||
spondyloarthritis,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
squamous cell,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|DERMATOLOGY|ENT|CLINICAL ONCOLOGY
|
||||
squamous cell carcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|DERMATOLOGY|ENT|CLINICAL ONCOLOGY
|
||||
stem cell transplant,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|TRANSPLANTATION SURGERY
|
||||
stroke,STROKE MEDICINE,STROKE MEDICINE|NEUROLOGY
|
||||
systemic lupus erythematosus,RHEUMATOLOGY,RHEUMATOLOGY|CLINICAL IMMUNOLOGY|NEPHROLOGY
|
||||
systemic mastocytosis,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|CLINICAL IMMUNOLOGY
|
||||
t-cell lymphoma,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|MEDICAL ONCOLOGY
|
||||
thalassaemia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|PAEDIATRICS
|
||||
thrombocytopenia,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
thrombocytopenic purpura,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
thromboembolism,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|CARDIOLOGY
|
||||
thrombotic thrombocytopenic purpura,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY
|
||||
thyroid cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|ENDOCRINOLOGY|CLINICAL ONCOLOGY
|
||||
tophaceous gout,RHEUMATOLOGY,RHEUMATOLOGY
|
||||
transitional cell carcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
transthyretin amyloidosis,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|CARDIOLOGY|NEUROLOGY
|
||||
triple-negative,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|BREAST SURGERY|CLINICAL ONCOLOGY
|
||||
tuberous sclerosis,NEUROLOGY,NEUROLOGY|PAEDIATRICS
|
||||
type 1 diabetes,DIABETIC MEDICINE,DIABETIC MEDICINE|ENDOCRINOLOGY|PAEDIATRICS
|
||||
type 2 diabetes,DIABETIC MEDICINE,DIABETIC MEDICINE|ENDOCRINOLOGY
|
||||
ulcerative colitis,GASTROENTEROLOGY,GASTROENTEROLOGY|COLORECTAL SURGERY
|
||||
urothelial,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
urothelial cancer,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
urothelial carcinoma,MEDICAL ONCOLOGY,MEDICAL ONCOLOGY|UROLOGY|CLINICAL ONCOLOGY
|
||||
urticaria,DERMATOLOGY,DERMATOLOGY|CLINICAL IMMUNOLOGY
|
||||
uterine fibroids,GYNAECOLOGY,GYNAECOLOGY
|
||||
uveitis,OPHTHALMOLOGY,OPHTHALMOLOGY|MEDICAL OPHTHALMOLOGY|RHEUMATOLOGY
|
||||
vascular disease,VASCULAR SURGERY,VASCULAR SURGERY|CARDIOLOGY
|
||||
vasculitis,RHEUMATOLOGY,RHEUMATOLOGY|CLINICAL IMMUNOLOGY
|
||||
venom allergy,CLINICAL IMMUNOLOGY,CLINICAL IMMUNOLOGY
|
||||
venous thromboembolism,CLINICAL HAEMATOLOGY,CLINICAL HAEMATOLOGY|VASCULAR SURGERY
|
||||
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
@@ -0,0 +1,186 @@
|
||||
-- NICE TA Indication SNOMED Mapping Query (v2 - optimized clusters)
|
||||
-- Excludes overly broad clusters (GDPPR_COD, GDPPR2YR_COD)
|
||||
|
||||
WITH SearchTermClusters AS (
|
||||
SELECT Search_Term, Cluster_ID FROM (VALUES
|
||||
('acute lymphoblastic leukaemia', 'HAEMCANMORPH_COD'),
|
||||
('acute myeloid leukaemia', 'C19HAEMCAN_COD'),
|
||||
('acute promyelocytic leukaemia', 'HAEMCANMORPH_COD'),
|
||||
('allergic asthma', 'AST_COD'),
|
||||
('allergic rhinitis', 'MILDINTAST_COD'),
|
||||
('alzheimer''s disease', 'DEMALZ_COD'),
|
||||
('amyloidosis', 'AMYLOID_COD'),
|
||||
('anaemia', 'eFI2_AnaemiaTimeSensitive'),
|
||||
('anaplastic large cell lymphoma', 'C19HAEMCAN_COD'),
|
||||
('apixaban', 'DOACCON_COD'),
|
||||
('aplastic anaemia', 'eFI2_AnaemiaEver'),
|
||||
('arthritis', 'eFI2_InflammatoryArthritis'),
|
||||
('asthma', 'eFI2_Asthma'),
|
||||
('atopic dermatitis', 'ATOPDERM_COD'),
|
||||
('atrial fibrillation', 'eFI2_AtrialFibrillation'),
|
||||
('attention deficit hyperactivity disorder', 'ADHD_COD'),
|
||||
('bipolar disorder', 'MH_COD'),
|
||||
('bladder', 'eFI2_UrinaryIncontinence'),
|
||||
('breast cancer', 'BRCANSCR_COD'),
|
||||
('cardiomyopathy', 'eFI2_HarmfulDrinking'),
|
||||
('cardiovascular disease', 'CVDRISKASS_COD'),
|
||||
('cervical cancer', 'CSDEC_COD'),
|
||||
('cholangiocarcinoma', 'eFI2_Cancer'),
|
||||
('chronic kidney disease', 'CKD_COD'),
|
||||
('chronic liver disease', 'eFI2_LiverProblems'),
|
||||
('chronic lymphocytic leukaemia', 'EPPHAEMCAN_COD'),
|
||||
('chronic myeloid leukaemia', 'EPPHAEMCAN_COD'),
|
||||
('chronic obstructive pulmonary disease', 'eFI2_COPD'),
|
||||
('colon cancer', 'eFI2_Cancer'),
|
||||
('colorectal cancer', 'GICANREF_COD'),
|
||||
('constipation', 'CHRONCONSTIP_COD'),
|
||||
('covid-19', 'POSSPOSTCOVID_COD'),
|
||||
('crohn''s disease', 'eFI2_InflammatoryBowelDisease'),
|
||||
('cutaneous t-cell lymphoma', 'C19HAEMCAN_COD'),
|
||||
('cystic fibrosis', 'CUST_ICB_CYSTIC_FIBROSIS'),
|
||||
('deep vein thrombosis', 'VTE_COD'),
|
||||
('depression', 'eFI2_Depression'),
|
||||
('diabetes', 'eFI2_DiabetesEver'),
|
||||
('diabetic retinopathy', 'DRSELIGIBILITY_COD'),
|
||||
('diffuse large b-cell lymphoma', 'C19HAEMCAN_COD'),
|
||||
('dravet syndrome', 'EPIL_COD'),
|
||||
('drug misuse', 'ILLSUBINT_COD'),
|
||||
('dyspepsia', 'eFI2_AbdominalPain'),
|
||||
('epilepsy', 'eFI2_Seizures'),
|
||||
('fallopian tube', 'STERIL_COD'),
|
||||
('follicular lymphoma', 'C19HAEMCAN_COD'),
|
||||
('gastric cancer', 'eFI2_Cancer'),
|
||||
('giant cell arteritis', 'GCA_COD'),
|
||||
('glioma', 'NHAEMCANMORPH_COD'),
|
||||
('gout', 'eFI2_InflammatoryArthritis'),
|
||||
('graft versus host disease', 'GVHD_COD'),
|
||||
('granulomatosis with polyangiitis', 'WEGENERVASC_COD'),
|
||||
('growth hormone deficiency', 'HYPOPITUITARY_COD'),
|
||||
('hand eczema', 'ECZEMA_COD'),
|
||||
('heart failure', 'eFI2_HeartFailure'),
|
||||
('hepatitis b', 'HEPBCVAC_COD'),
|
||||
('hepatocellular carcinoma', 'eFI2_Cancer'),
|
||||
('hiv', 'PREFLANG_COD'),
|
||||
('hodgkin lymphoma', 'HAEMCANMORPH_COD'),
|
||||
('hormone receptor', 'eFI2_ThyroidProblems'),
|
||||
('hypercholesterolaemia', 'CLASSFH_COD'),
|
||||
('immune thrombocytopenia', 'ITP_COD'),
|
||||
('influenza', 'FLUINVITE_COD'),
|
||||
('insomnia', 'eFI2_SleepProblems'),
|
||||
('irritable bowel syndrome', 'IBS_COD'),
|
||||
('ischaemic stroke', 'OSTR_COD'),
|
||||
('juvenile idiopathic arthritis', 'RARTHAD_COD'),
|
||||
('kidney transplant', 'RENALTRANSP_COD'),
|
||||
('leukaemia', 'eFI2_Cancer'),
|
||||
('lung cancer', 'FTCANREF_COD'),
|
||||
('lymphoma', 'C19HAEMCAN_COD'),
|
||||
('macular degeneration', 'CUST_ICB_VISUAL_IMPAIRMENT'),
|
||||
('macular oedema', 'CUST_ICB_VISUAL_IMPAIRMENT'),
|
||||
('major depressive episodes', 'eFI2_Depression'),
|
||||
('malignant melanoma', 'eFI2_Cancer'),
|
||||
('malignant pleural mesothelioma', 'LUNGCAN_COD'),
|
||||
('manic episode', 'MH_COD'),
|
||||
('mantle cell lymphoma', 'HAEMCANMORPH_COD'),
|
||||
('melanoma', 'eFI2_Cancer'),
|
||||
('merkel cell carcinoma', 'C19CAN_COD'),
|
||||
('migraine', 'eFI2_Headache'),
|
||||
('motor neurone disease', 'MND_COD'),
|
||||
('multiple myeloma', 'C19HAEMCAN_COD'),
|
||||
('multiple sclerosis', 'MS_COD'),
|
||||
('myelodysplastic', 'eFI2_AnaemiaEver'),
|
||||
('myelofibrosis', 'MDS_COD'),
|
||||
('myocardial infarction', 'eFI2_IschaemicHeartDisease'),
|
||||
('myotonia', 'CNDATRISK2_COD'),
|
||||
('narcolepsy', 'LD_COD'),
|
||||
('neuroendocrine tumour', 'LUNGCAN_COD'),
|
||||
('non-small cell lung cancer', 'LUNGCAN_COD'),
|
||||
('non-small-cell lung cancer', 'FTCANREF_COD'),
|
||||
('obesity', 'BMI30_COD'),
|
||||
('osteoarthritis', 'CUST_ICB_OSTEOARTHRITIS'),
|
||||
('osteoporosis', 'eFI2_Osteoporosis'),
|
||||
('osteosarcoma', 'NHAEMCANMORPH_COD'),
|
||||
('ovarian cancer', 'C19CAN_COD'),
|
||||
('peripheral arterial disease', 'PADEXC_COD'),
|
||||
('plaque psoriasis', 'PSORIASIS_COD'),
|
||||
('polycystic kidney disease', 'EPPCONGMALF_COD'),
|
||||
('polycythaemia vera', 'C19HAEMCAN_COD'),
|
||||
('pregnancy', 'C19PREG_COD'),
|
||||
('primary biliary cholangitis', 'eFI2_LiverProblems'),
|
||||
('primary hypercholesterolaemia', 'FNFHYP_COD'),
|
||||
('prostate cancer', 'EPPSOLIDCAN_COD'),
|
||||
('psoriasis', 'PSORIASIS_COD'),
|
||||
('psoriatic arthritis', 'RARTHAD_COD'),
|
||||
('pulmonary embolism', 'eFI2_RespiratoryDiseaseTimeSensitive'),
|
||||
('pulmonary fibrosis', 'ILD_COD'),
|
||||
('relapsing multiple sclerosis', 'MS_COD'),
|
||||
('renal cell carcinoma', 'C19CAN_COD'),
|
||||
('renal transplantation', 'RENALTRANSP_COD'),
|
||||
('retinal vein occlusion', 'CUST_ICB_VISUAL_IMPAIRMENT'),
|
||||
('rheumatoid arthritis', 'eFI2_InflammatoryArthritis'),
|
||||
('rivaroxaban', 'DOACCON_COD'),
|
||||
('schizophrenia', 'MH_COD'),
|
||||
('seizures', 'LSZFREQ_COD'),
|
||||
('sepsis', 'C19ACTIVITY_COD'),
|
||||
('severe persistent allergic asthma', 'SEVAST_COD'),
|
||||
('sickle cell disease', 'SICKLE_COD'),
|
||||
('sleep apnoea', 'CUST_ICB_NON_SEVERE_LDA'),
|
||||
('smoking cessation', 'SMOKINGINT_COD'),
|
||||
('soft tissue sarcoma', 'NHAEMCANMORPH_COD'),
|
||||
('spinal muscular atrophy', 'MND_COD'),
|
||||
('squamous cell', 'C19CAN_COD'),
|
||||
('squamous cell carcinoma', 'C19CAN_COD'),
|
||||
('stem cell transplant', 'ALLOTRANSP_COD'),
|
||||
('stroke', 'eFI2_Stroke'),
|
||||
('systemic lupus erythematosus', 'SLUPUS_COD'),
|
||||
('systemic mastocytosis', 'HAEMCANMORPH_COD'),
|
||||
('thrombocytopenic purpura', 'TTP_COD'),
|
||||
('thrombotic thrombocytopenic purpura', 'TTP_COD'),
|
||||
('thyroid cancer', 'C19CAN_COD'),
|
||||
('tophaceous gout', 'CUST_ICB_OSTEOARTHRITIS'),
|
||||
('transitional cell carcinoma', 'C19CAN_COD'),
|
||||
('type 1 diabetes', 'DMTYPE1_COD'),
|
||||
('type 2 diabetes', 'DMTYPE2_COD'),
|
||||
('ulcerative colitis', 'eFI2_InflammatoryBowelDisease'),
|
||||
('urothelial carcinoma', 'NHAEMCANMORPH_COD'),
|
||||
('urticaria', 'XSAL_COD'),
|
||||
('uveitis', 'CUST_ICB_VISUAL_IMPAIRMENT'),
|
||||
('vascular disease', 'CVDINVITE_COD'),
|
||||
('vasculitis', 'CRYOGLOBVASC_COD')
|
||||
) AS t(Search_Term, Cluster_ID)
|
||||
),
|
||||
|
||||
ClusterCodes AS (
|
||||
SELECT
|
||||
stc.Search_Term,
|
||||
c."SNOMEDCode",
|
||||
c."SNOMEDDescription"
|
||||
FROM SearchTermClusters stc
|
||||
JOIN DATA_HUB.PHM."ClinicalCodingClusterSnomedCodes" c
|
||||
ON stc.Cluster_ID = c."Cluster_ID"
|
||||
WHERE c."SNOMEDCode" IS NOT NULL
|
||||
),
|
||||
|
||||
ExplicitCodes AS (
|
||||
SELECT Search_Term, SNOMEDCode, SNOMEDDescription FROM (VALUES
|
||||
('acute coronary syndrome', '837091000000100', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '162930007', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '239805001', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '239810002', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '239811003', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '394990003', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '429712009', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '441562009', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '441680005', 'Manual mapping'),
|
||||
('ankylosing spondylitis', '441930001', 'Manual mapping'),
|
||||
('axial spondyloarthritis', '723116002', 'Manual mapping'),
|
||||
('choroidal neovascularisation', '380621000000102', 'Manual mapping'),
|
||||
('choroidal neovascularisation', '733124000', 'Manual mapping')
|
||||
) AS t(Search_Term, SNOMEDCode, SNOMEDDescription)
|
||||
)
|
||||
|
||||
SELECT Search_Term, "SNOMEDCode" AS SNOMEDCode, "SNOMEDDescription" AS SNOMEDDescription
|
||||
FROM ClusterCodes
|
||||
UNION ALL
|
||||
SELECT Search_Term, SNOMEDCode, SNOMEDDescription
|
||||
FROM ExplicitCodes
|
||||
ORDER BY Search_Term, SNOMEDCode;
|
||||
Binary file not shown.
Reference in New Issue
Block a user