用疾病严重程度知识图谱增强病历，提升临床风险预测准确率
Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation
- 构建带严重程度标注的医学知识图谱，融合文献与病历数据
- 在MIMIC-III/IV上，死亡预测宏F1提升28.5%，再入院预测提升19.7%
- 适合临床决策支持、重症预警等医疗AI应用研究者
电子健康记录（EHR）虽包含丰富临床数据，但如何有效融合异构外部知识以预测患者临床风险仍具挑战。现有方法因数据稀疏及未充分利用非结构化临床笔记，难以捕捉疾病严重程度、治疗反应和复杂临床进展。为此，我们提出TRACER框架：(1) 构建基于医学文献的带严重程度信息的知识图谱；(2) 从知识图谱中检索与患者病情进展相关的严重度加权路径；(3) 从非结构化临床笔记中提取关键事件；(4) 通过相似病患案例增强患者上下文。在MIMIC-III和MIMIC-IV数据集上的实验表明，该方法显著优于现有最优基线，死亡预测任务的宏F1提升最高达28.5%，再入院预测任务提升19.7%。
原文摘要 · Abstract (English)
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.
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