AIPatient KG: MIMIC-III and CORAL Electronic Health Records based Patient Knowledge Graph

This study integrates the MIMIC-III and CORAL electronic health records into
knowledge graphs to enhance their utility for advanced medical analysis and
decision-making. MIMIC-III contains comprehensive data from over 40,000
patients, while CORAL focuses on oncology-specific information from 40
patients, aiding in complex medical reasoning. We used a LLM (Large Language
Model)-based Named Entity Recognition approach to extract relevant medical
information from these datasets, independently verified by domain experts, and
constructed the AIPatient and CORAL Knowledge Graph in Neo4j. This graph
supports the AIPatient system, which simulates patient interactions for
advanced decision support. Additionally, we introduce MIMIC-III and CORAL
Question and Answering sets, which are created for evaluating system
performance such as accuracy, robustness and stability.