Graph-native diagnostic decision support — Bayesian inference over a clinical knowledge graph.
Zebra Diagnostics is the company I'm building full-time. The mission is to save 200,000 lives in 8 years by making medical diagnosis less of a coin flip on the first visit.
The reasoning core is `bray` — a Bayesian inference microservice that takes patient evidence, looks up priors and likelihoods from a SNOMED CT– and LOINC-aligned knowledge graph, and produces patient-specific posterior probabilities over candidate clinical problems. Around it sits `docdoc`, the React Brief Studio that renders the clinician brief, evidence timeline, and CTA feed.
The system is designed for interoperability — clinical data flows in via FHIR (see `qhint`, the Redox/QHIN onramp), terminology is normalized through `SNOAPI`, and `ligature` orchestrates the Docker-based medical microservices behind a single control plane.