EHR2Trace：面向患者世界模型与临床智能体的可审计电子健康记录数据基础设施
EHR2Trace: Auditable EHR Data Infrastructure for Patient World Models and Clinical Agents
浏览论文内容
中文总结 AI 辅助
EHR2Trace将异构电子健康记录转换为可追踪事件，支持模型训练与评估，并通过分离事件时间与信息可用性、自动化验证，提升临床智能体的数据可靠性与可审计性。
中文摘要 AI 辅助
患者世界模型和临床智能体旨在预测患者健康状况的变化并支持临床工作。开发这些系统需要可靠的病史、治疗记录以及每个决策点可用信息的记录。电子健康记录（EHRs）包含这些历史信息，但事件记录方式的差异使其难以被一致地使用。我们提出了EHR2Trace，一个将来自不同来源的电子健康记录转换为可追踪的患者事件以用于模型训练和评估的系统。它将事件与源记录关联，将事件时间与信息可用性分离，并区分用药医嘱、配药和给药。一种共享的事件表示支持OMOP和MEDS两种导出格式，并具备自动化验证和可复现的构建流程。在三个临床数据集上，EHR2Trace转换了8.464亿个事件，除MIMIC-IV上的一项单位一致性检查外，所有适用的检查均通过，并检测出了所有28个注入的故障。一项受控预测实验表明，将较晚的诊断分配到入院时间会显著夸大测量的性能，而基于此类数据训练的模型在按可用性过滤的历史数据上部署时会损失准确性。EHR2Trace为患者世界模型和临床智能体提供了可复用的数据基础，帮助研究人员检查患者历史、审查转换决策，并使用明确的数据规则评估模型。
英文摘要
Patient world models and clinical agents aim to predict changes in patients' health and support clinical work. Developing these systems requires reliable histories of patient conditions, treatments, and the information available at each decision. Electronic health records (EHRs) contain these histories, but differences in how events are recorded make them difficult to use consistently. We present EHR2Trace, a system that converts EHRs from different sources into traceable patient events for model training and evaluation. It links events to source records, separates event time from information availability, and distinguishes medication orders, dispensing, and administration. A shared event representation supports both OMOP and MEDS exports, with automated validation and reproducible builds. Across three clinical datasets, EHR2Trace converted 846.4 million events, with every applicable check passing except one unit-consistency check on MIMIC-IV, and detected all 28 injected faults. A controlled prediction experiment showed that assigning later diagnoses to admission time substantially inflated measured performance, and that a model trained on such data lost accuracy when deployed on histories filtered by availability. EHR2Trace provides a reusable data foundation for patient world models and clinical agents, helping researchers inspect patient histories, check conversion decisions, and evaluate models with explicit data rules.
发表机构
- University of Colorado Anschutz School of Medicine（科罗拉多大学安舒茨医学院）
- Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
