MedQGraph: TMKGs for AI-Driven Healthcare Insights

Clinical decision-making depends on Electronic Health Records (EHRs), but their complexity hinders efficient extraction and analysis. Traditional knowledge graph methods focus mainly on structured data and static relationships, limiting advanced querying. We present the Medical Record Knowledge Graph (MRKG), a scalable framework that transforms structured MIMIC-IV EHR data into an interpretable, queryable graph using Neo4j. MRKG integrates diverse clinical entities, diagnoses, procedures, and medications into a cohesive structure, enabling comprehensive exploration of patient history.

Speakers: Isaac Ritharson & Ishan Chaudhary

Resources:
Get Started with Aura – https://bit.ly/3LOLrjh
Deployment Center – https://bit.ly/4jOelM3
Ground AI Systems and Agents with Neo4j – https://bit.ly/4oVsnyb

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