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.

搜集汇总

背景与挑战

背景概述

该数据集是基于MIMIC-III和CORAL电子健康记录构建的患者知识图谱，包含两个子图谱：AIPatient KG（56名MIMIC-III患者）和CORAL KG（40名肿瘤患者）。数据集采用LLM进行医疗实体识别，构建了包含症状、病史、过敏等节点的结构化知识图谱，支持医疗AI推理、临床决策和医学教育应用。