NODES 2025 - MedQGraph: TMKGs for AI-Driven Healthcare Insights

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This course includes

51.3 hours of video
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Summary

Keywords

neo4j graph databases graphs LLM retrieval augmented generation RAG vector database vector search GenAI AI Artificial intelligence nodes25

Full Transcript

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 View Presentation: https://drive.google.com/file/d/1aQaKPjXJO6SInkawWrkH18M66E_zafil/view?usp=drive_link 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 #nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph