Maternal Mortality Review Committee (MMRC) Findings: an open dataset (Hidden Health Data)

Hidden Health Data turns buried public-record health documents (PDFs) into research-ready, provenance-stamped datasets via large language model extraction (Anthropic Claude claude-haiku-4-5) with a verification layer: source grounding, symbolic + Logical Neural Network-inspired neurosymbolic constraints, a heterogeneous ensemble, and a conformal selective-acceptance gate. Source documents are U.S. state Maternal Mortality Review Committee (MMRC) reports — also called Maternal Mortality Review or Pregnancy-Associated Mortality Review reports — which review pregnancy-associated/maternal deaths and issue findings and prevention recommendations. This release (v0.4.0) expands coverage from 6 to near-national: 6,303 records from 54 source documents across 51 jurisdictions (49 states, the District of Columbia, and a multi-state CDC aggregate; only New Hampshire lacked a public PDF report): 3,351 recommendations, 2,952 findings. Every record carries full provenance (source-document SHA-256, page, model, confidence) and a quality/trust score; nothing is imputed or dropped. These records are AI-extracted and not yet independently validated — treat as preliminary, filter on the trust score, and verify values against the linked source pages. Derived entirely from public-record documents; no protected health information. Code (Apache-2.0): github.com/sanjaybasu/dark-health-data. Data license: CC0-1.0.