Bayesian Pattern Recognition for Real World Applications
📚 View all posts in the Graph-based Healthcare Series
Graph-based Healthcare Series — 5
This is the fifth post in an ongoing series on graph-based healthcare tools. Stay tuned for upcoming entries on clinical modeling, decision support systems, and graph-powered AI assistants.
In our previous post, we explored how large language models (LLMs) can simulate realistic pediatric patient encounters based on the IMNCI guidelines. These synthetic notes were grounded in real clinical logic, labeled with structured IMNCI classifications, and validated using a multi-agent verification strategy inspired by the Bayesian Truth Serum (BTS). The result: a high-fidelity dataset of richly annotated, clinically plausible pediatric cases.
In this post, we put that dataset to work—prototyping a Bayesian diagnostic engine that quantifies clinical evidence, scores conditions, and updates probabilities in a way that mirrors how clinicians think.