feat: integrate drug-aware indication matching into refresh pipeline (Task 3.1)
Replace old per-patient indication matching in refresh_pathways.py with drug-aware matching via assign_drug_indications(). Each drug is now cross-referenced against both the patient's GP diagnoses AND the DimSearchTerm.csv drug mapping. GP codes restricted to HCD data window via earliest_hcd_date parameter.
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Andrew Charlwood
@@ -143,14 +143,14 @@ Only assign a drug to an indication if BOTH conditions are met. If a patient's d
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## Phase 3: Pipeline Integration
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### 3.1 Update `refresh_pathways.py` indication processing
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- [ ] In the `elif current_chart_type == "indication":` block:
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- [x] In the `elif current_chart_type == "indication":` block:
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1. Call `get_patient_indication_groups()` as before (but now returns ALL matches)
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2. Load drug mapping: `drug_mapping = load_drug_indication_mapping()`
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3. Call `assign_drug_indications(df, gp_matches_df, drug_mapping)`
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4. Use modified_df (with indication-aware UPIDs) for pathway processing
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5. Use indication_df for the indication mapping
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- [ ] Pass modified_df (not original df) to `process_indication_pathway_for_date_filter()`
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- [ ] Verify: Pipeline compiles, `python -m py_compile cli/refresh_pathways.py`
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- [x] Pass modified_df (not original df) to `process_indication_pathway_for_date_filter()`
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- [x] Verify: Pipeline compiles, `python -m py_compile cli/refresh_pathways.py`
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### 3.2 Test with dry run
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- [ ] Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v`
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+42
-95
@@ -48,7 +48,11 @@ from data_processing.pathway_pipeline import (
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extract_indication_fields,
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convert_to_records,
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)
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from data_processing.diagnosis_lookup import get_patient_indication_groups
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from data_processing.diagnosis_lookup import (
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assign_drug_indications,
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get_patient_indication_groups,
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load_drug_indication_mapping,
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)
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logger = get_logger(__name__)
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@@ -359,9 +363,11 @@ def refresh_pathways(
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results[f"{filter_id}:directory"] = records
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elif current_chart_type == "indication":
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# For indication charts, we need to look up GP diagnoses for all patients
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# using the new Snowflake-direct approach via get_patient_indication_groups()
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logger.info("Building indication groups from GP diagnosis lookups (Snowflake-direct)...")
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# For indication charts, use drug-aware matching:
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# 1. Get ALL GP diagnosis matches per patient (with code_frequency)
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# 2. Cross-reference with drug-to-Search_Term mapping from DimSearchTerm.csv
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# 3. Assign each drug to its matched indication via modified UPIDs
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logger.info("Building drug-aware indication groups...")
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# Check Snowflake availability
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from data_processing.snowflake_connector import get_connector, is_snowflake_available
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@@ -376,115 +382,60 @@ def refresh_pathways(
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import pandas as pd
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connector = get_connector()
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# Step 1: Extract unique PseudoNHSNoLinked values from df
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# This is the patient identifier that matches PatientPseudonym in GP records
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if 'PseudoNHSNoLinked' not in df.columns:
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logger.error("DataFrame missing 'PseudoNHSNoLinked' column - cannot lookup GP records")
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for config in DATE_FILTER_CONFIGS:
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results[f"{config.id}:indication"] = []
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continue
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# Get unique patient pseudonyms for GP lookup (avoid redundant queries)
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patient_pseudonyms = df['PseudoNHSNoLinked'].dropna().unique().tolist()
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# Step 1: Load drug-to-Search_Term mapping from DimSearchTerm.csv
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_, search_term_to_fragments = load_drug_indication_mapping()
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logger.info(f"Loaded drug mapping: {len(search_term_to_fragments)} Search_Terms")
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# Step 2: Get ALL GP diagnosis matches per patient (with code_frequency)
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patient_pseudonyms = df['PseudoNHSNoLinked'].dropna().unique().tolist()
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logger.info(f"Looking up GP diagnoses for {len(patient_pseudonyms)} unique patients...")
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# Step 2: Call the new Snowflake-direct indication lookup
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# Restrict GP codes to HCD data window (reduces noise from old diagnoses)
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earliest_hcd_date = df['Intervention Date'].min()
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if pd.notna(earliest_hcd_date):
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earliest_hcd_date_str = pd.Timestamp(earliest_hcd_date).strftime('%Y-%m-%d')
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logger.info(f"Restricting GP codes to HCD window: >= {earliest_hcd_date_str}")
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else:
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earliest_hcd_date_str = None
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gp_matches_df = get_patient_indication_groups(
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patient_pseudonyms=patient_pseudonyms,
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connector=connector,
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batch_size=500,
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earliest_hcd_date=earliest_hcd_date_str,
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)
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# Step 3: Build indication_df mapping UPID -> Indication_Group
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# For matched patients: Indication_Group = Search_Term
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# For unmatched patients: Indication_Group = Directory + " (no GP dx)"
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#
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# IMPORTANT: We need ALL unique UPIDs, not just unique PseudoNHSNoLinked.
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# A patient can have multiple UPIDs if they visited multiple providers.
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# Step 3: Assign drug-aware indications using cross-referencing
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# This replaces the old per-patient approach with per-drug matching
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modified_df, indication_df = assign_drug_indications(
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df=df,
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gp_matches_df=gp_matches_df,
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search_term_to_fragments=search_term_to_fragments,
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)
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# Get all unique UPID records with their PseudoNHSNoLinked and Directory
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upid_lookup = df[['UPID', 'PseudoNHSNoLinked', 'Directory']].drop_duplicates(
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subset=['UPID']
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).copy()
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logger.info(f"Drug-aware indication matching complete. "
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f"Modified UPIDs: {modified_df['UPID'].nunique()}, "
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f"Indication groups: {len(indication_df)}")
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if gp_matches_df.empty:
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logger.warning("No GP matches found - all patients will use fallback directory")
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# All patients use fallback
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indication_records = []
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for _, row in upid_lookup.iterrows():
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directory = row['Directory']
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indication_records.append({
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'UPID': row['UPID'],
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'Indication_Group': str(directory) + " (no GP dx)" if pd.notna(directory) else "UNKNOWN (no GP dx)",
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'Source': 'FALLBACK',
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})
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indication_df = pd.DataFrame(indication_records)
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if indication_df.empty:
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logger.warning("Empty indication_df - skipping indication charts")
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for config in DATE_FILTER_CONFIGS:
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results[f"{config.id}:indication"] = []
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else:
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# Create lookup dict: PseudoNHSNoLinked -> Search_Term
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match_lookup = dict(zip(
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gp_matches_df['PatientPseudonym'],
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gp_matches_df['Search_Term']
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))
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# Build indication records for each unique UPID
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indication_records = []
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for _, row in upid_lookup.iterrows():
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pseudo = row['PseudoNHSNoLinked']
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upid = row['UPID']
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directory = row['Directory']
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if pd.notna(pseudo) and pseudo in match_lookup:
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indication_records.append({
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'UPID': upid,
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'Indication_Group': match_lookup[pseudo],
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'Source': 'DIAGNOSIS',
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})
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else:
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# Use fallback: Directory + " (no GP dx)"
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fallback_label = str(directory) + " (no GP dx)" if pd.notna(directory) else "UNKNOWN (no GP dx)"
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indication_records.append({
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'UPID': upid,
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'Indication_Group': fallback_label,
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'Source': 'FALLBACK',
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})
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indication_df = pd.DataFrame(indication_records)
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# Log coverage statistics
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if not indication_df.empty:
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diagnosis_count = (indication_df['Source'] == 'DIAGNOSIS').sum()
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fallback_count = (indication_df['Source'] == 'FALLBACK').sum()
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total = len(indication_df)
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stats["diagnosis_coverage"] = {
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"diagnosis": int(diagnosis_count),
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"fallback": int(fallback_count),
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"total": total,
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"diagnosis_pct": round(100 * diagnosis_count / total, 1) if total > 0 else 0,
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}
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logger.info(f"Indication coverage: {diagnosis_count}/{total} ({stats['diagnosis_coverage']['diagnosis_pct']}%) diagnosis-matched")
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# Log top indication groups
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top_indications = indication_df[indication_df['Source'] == 'DIAGNOSIS']['Indication_Group'].value_counts().head(5)
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if len(top_indications) > 0:
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logger.info(f"Top 5 indications: {dict(top_indications)}")
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# Rename column for compatibility with generate_icicle_chart_indication
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# It expects indication_df to have 'Directory' column (mapped from Indication_Group)
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indication_df_for_chart = indication_df[['UPID', 'Indication_Group']].copy()
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indication_df_for_chart = indication_df_for_chart.rename(columns={'Indication_Group': 'Directory'})
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# Ensure unique UPID index (build_hierarchy requires uniquely valued Index)
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# Keep first occurrence - DIAGNOSIS entries should come before FALLBACK
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indication_df_for_chart = indication_df_for_chart.drop_duplicates(subset=['UPID'], keep='first')
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indication_df_for_chart = indication_df_for_chart.set_index('UPID')
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# Process each date filter with indication grouping
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# Process each date filter with drug-aware indication grouping
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# Use modified_df (with indication-aware UPIDs) instead of original df
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for config in DATE_FILTER_CONFIGS:
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logger.info(f"Processing indication pathway for {config.id}")
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ice_df = process_indication_pathway_for_date_filter(
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df=df,
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indication_df=indication_df_for_chart,
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df=modified_df,
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indication_df=indication_df,
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config=config,
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trust_filter=trust_filter,
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drug_filter=drug_filter,
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@@ -506,10 +457,6 @@ def refresh_pathways(
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results[f"{config.id}:indication"] = records
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logger.info(f"Completed {config.id}:indication: {len(records)} nodes")
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else:
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logger.warning("Empty indication_df - skipping indication charts")
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for config in DATE_FILTER_CONFIGS:
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results[f"{config.id}:indication"] = []
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except Exception as e:
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logger.error(f"Error processing indication charts: {e}")
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@@ -253,3 +253,49 @@ This project extends the indication-based pathway charts (Phase 1-5 complete) wi
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- Can verify with py_compile; full Snowflake test via --dry-run
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### Blocked items:
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- None
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## Iteration 5 — 2026-02-05
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### Task: 3.1 — Update refresh_pathways.py indication processing to use assign_drug_indications()
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### Why this task:
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- All Phase 1 & 2 dependencies complete (query returns all matches, drug mapping loaded, assign_drug_indications() exists)
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- Task 3.1 is the pipeline integration step — wires the new drug-aware matching into the actual refresh pipeline
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- Must be done before Task 3.2 (dry run test) which validates the integrated pipeline
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### Status: COMPLETE
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### What was done:
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- Updated imports at top of `cli/refresh_pathways.py`:
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- Added `assign_drug_indications` and `load_drug_indication_mapping` from `data_processing.diagnosis_lookup`
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- Replaced the entire indication processing block (old ~90 lines → new ~60 lines):
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- **Old approach**: `dict(zip(gp_matches_df['PatientPseudonym'], gp_matches_df['Search_Term']))` — only kept LAST match per patient, no drug awareness
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- **New approach**:
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1. `load_drug_indication_mapping()` → `search_term_to_fragments`
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2. Compute `earliest_hcd_date` from `df['Intervention Date'].min()` as ISO string
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3. `get_patient_indication_groups(earliest_hcd_date=earliest_hcd_date_str)` → all GP matches with code_frequency
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4. `assign_drug_indications(df, gp_matches_df, search_term_to_fragments)` → `(modified_df, indication_df)`
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5. Pass `modified_df` (not original `df`) to `process_indication_pathway_for_date_filter()`
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6. `indication_df` already indexed by modified UPID with 'Directory' column — directly compatible
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- Removed: old `match_lookup`, `upid_lookup`, manual `indication_records` building, `indication_df_for_chart` renaming
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- Kept: Snowflake availability check, PseudoNHSNoLinked column check, error handling, date filter loop
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### Validation results:
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- Tier 1 (Code): py_compile PASSED, individual imports PASSED, full module import PASSED
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- Tier 2 (Data): N/A — requires live Snowflake for dry run test (Task 3.2)
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- Tier 3 (Functional): N/A — no UI changes
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### Files changed:
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- cli/refresh_pathways.py (updated imports, replaced indication processing block)
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- IMPLEMENTATION_PLAN.md (marked 3.1 subtasks [x])
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### Committed: [pending]
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### Patterns discovered:
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- `assign_drug_indications()` returns `indication_df` already indexed by modified UPID with 'Directory' column — no need for intermediate renaming/reindexing steps that the old code required
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- `earliest_hcd_date` must be converted via `pd.Timestamp(...).strftime('%Y-%m-%d')` because `df['Intervention Date'].min()` may return a Timestamp or string depending on data source
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- The old code had a "stats['diagnosis_coverage']" tracking block — this is now handled internally by `assign_drug_indications()` logging. If stats tracking in the return dict is needed later, can add it back.
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### Next iteration should:
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- Work on Task 3.2: Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v`
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- This requires a live Snowflake connection
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- Verify: modified UPIDs appear in logs, match rates logged, pathway nodes generated
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- If dry run passes, move to Phase 4 (full refresh + validation)
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- Key things to check in dry run output:
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- "Drug-aware indication matching complete" log message with match/fallback counts
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- "Modified UPIDs" count should be HIGHER than unique patient count (patients with multiple drugs for different indications)
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- Pathway node counts for indication charts should be in same ballpark as before (~300 per date filter)
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- No errors in indication pathway processing
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### Blocked items:
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- None
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