通过基于严重程度的知识图谱和检索增强生成进行轨迹感知临床风险预测
Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation
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中文总结 AI 辅助
针对利用EHRs预测临床风险的挑战，提出TRACER框架，通过构建含严重程度信息的知识图谱、检索患者进展路径、提取临床事件及用相似病例增强患者背景来预测风险，在相关数据集实验中相比基线有显著提升。
中文摘要 AI 辅助
虽然电子健康记录（EHRs）提供了丰富的临床数据，但利用异构外部知识有效增强患者记录以预测临床风险仍是重大挑战。现有方法因数据稀疏和非结构化临床笔记利用不足，无法捕捉疾病严重程度、治疗反应和细微临床进展。为此提出TRACER框架，它构建含医学文献严重程度信息的知识图谱，检索患者进展的临床相关、严重程度加权路径，从非结构化笔记中提取相关事件，并用相似病例增强患者背景。在MIMIC-III和MIMIC-IV数据集上的实验表明，相较于现有基线有显著提升，死亡率预测任务的Macro F1分数最高提高28.5%，再入院预测任务提高19.7%。
英文摘要
While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge. Existing methods fail to capture disease severity, treatment responses, and nuanced clinical progression, due to data sparsity and the underutilization of unstructured clinical notes. To address these challenges, we propose TRACER (a trajectory-aware and clinically grounded prediction framework) that (1) constructs a medical knowledge graph enriched with severity information from medical literature, (2) retrieves clinically relevant, severity-weighted paths of a patient's progression from the knowledge graph, (3) extracts clinically relevant events from unstructured clinical notes, and (4) augments patient context with similar peer cases. Experiments on the MIMIC-III and MIMIC-IV datasets demonstrate large gains over state-of-the-art baselines, with up to 28.5% increase in Macro F1 score for the mortality prediction task, and 19.7% increase for the readmission prediction task.
发表机构
- Hanyang University（汉阳大学）
- Korea University（韩国大学）
机构由 AI 辅助整理，请以论文原文为准。
