Simulating Real-World Pediatric Encounters Using Large Language Models
📚 View all posts in the Graph-based Healthcare Series
Graph-based Healthcare Series — 4
This is the fourth 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 demonstrated how agentic flows can transform diagnosis from a reactive retrieval task into a guided, context-aware reasoning process. By orchestrating modular assistants, tracking physician intent, and dynamically adapting based on feedback, we built a collaborative diagnostic experience that’s explainable, flexible, and clinically grounded.
In this post, we shift focus to the synthetic data generation side of the equation. We detail the steps taken to generate a diverse set of synthetic patient cases—each featuring unique symptoms, conditions, and diagnostic paths. These examples simulate a wide range of realistic clinical scenarios, laying the foundation for applying Bayesian pattern recognition methods to richly structured, verifiable patient data.