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0.7.4: Crosswalk 16→55 HCCs + broadened NER/aliases + tightened LLM prompt
Crosswalk (data/reference/) - hcc_v28_labels.csv: 16 → 55 HCCs across Cancer (8/9/10/11/12), Metabolic (21/22/23), Mental (54–58), Neuro (70–79), Infection (1/2/6), Skin (157/158), Acute cardio/pulmonary (82/86/87), Status (176/189), Hematology (46/47), GI (33/34), Liver (36 added as separate from 35), Kidney (135 acute added). All RAF weights marked PLACEHOLDER. - hcc_v28_crosswalk.csv: 51 → 220 ICD-10 rows covering the new HCCs. - hcc_v28_hierarchy.csv: 11 → 38 trumping edges (cancer, liver, hematology, mental, spinal, ischemic-heart, skin, infection, metabolic, GI). - icd10_cm_descriptions.csv: 54 → 220 descriptions. Integrity: crosswalk HCCs all have labels; every ICD-10 has a description; hierarchy references only known HCCs. Verified via one-off Python check. NER vocab (DictionaryNER) - Added ~50 new surface-form groups: cancer (colon, lung, breast, prostate, melanoma, leukemia, lymphoma, myeloma, metastatic), malnutrition, morbid obesity, alcohol/opioid dependenc…79938b6 -
Milestone 0.3.0: coder-voiced README, benchmark button, NER feature flag, CMS fetcher
README - Opening rewritten in coder vocabulary (suspect list / capture / MEAT / RADV risk / hierarchy trumping). Targets the S4 30-second recruiter skim. Benchmark endpoint + UI - POST /api/benchmark re-runs every gallery item live and returns min/median/p95/max/mean ms + per-case timings. Surfaces the S2 target (8000 ms p95 on CPU free tier) explicitly. - MetricsTab now has a "Run gallery benchmark" button with a red/green p95 card and a collapsible per-case breakdown. NER backend feature flag - New TransformersNER wrapper around the HF token-classification pipeline with lazy import + label normalization onto SpanType. - default_ner() reads HCC_MEAT_NER_BACKEND (dictionary|transformers) and HCC_MEAT_NER_MODEL (default blaze999/Medical-NER). - test_ner_backend_selection.py asserts selector behavior without importing transformers when the env var is unset — keeps CI lean. CMS fetcher - scripts/fetch_cms_hcc_v28.py now has two subcommands: inspect — download the CMS …e43bca6 -
Initial scaffold: HCC-MEAT Coder v0.1.0
End-to-end runnable pipeline with rule-based baselines in place of ML components, a FastAPI backend serving a React three-panel UI, and a 10-case regression suite guarding the invariants from AGENT_BRIEF.md §3 (faithfulness, negation/family-history filtering, hierarchy trumping, transparent risk). Deterministic components (real, tested): - E — HCC crosswalk + trumping over CMS-HCC v28 seed tables - H — over-coding (RADV) risk heuristic with named components + reasons - I — faithfulness verifier (substring guarantee + NLI hook) Baseline / swappable components: - A segmenter, B dictionary NER, C negation, D exact/alias linker, F rule-based MEAT extractor, G identity calibrator Infra: uv-driven pyproject, ruff + pytest + pre-commit, GH Actions CI for Python and UI, multi-stage Dockerfile for the HF Docker Space, typer CLIs for pipeline (`hcc-meat`) and evaluation (`hcc-meat-eval`). Reference data in data/reference/ uses PLACEHOLDER RAF weights; hydrate via scripts/fetch_cms_hcc_v28.…c6cfe5f
