AI 中文总结
MiGHT-EHR是针对异构时序EHR的多任务图变换器，在MIMIC-III/IV数据集上的四项临床预测任务中优于现有最优方法，且表示具有临床可解释性。
AI 中文摘要
从电子健康记录（EHR）中学习因具备改善临床预测的潜力而受到广泛关注，但由于EHR编码了异构、时序有序的临床交互，有效学习仍面临挑战。具体而言，EHR包含：（i）异构临床实体，包括患者、就诊、诊断、处方和操作，以及它们之间的异构交互；（ii）跨医院就诊的纵向患者轨迹；（iii）相关临床预测任务间的共享统计依赖关系。现有EHR学习方法仅能捕捉其中部分属性。为弥合这一差距，我们提出面向异构时序EHR的多任务图变换器（MiGHT-EHR），其在统一表示学习方法中同时对上述三种属性进行建模。MiGHT-EHR从EHR构建异构图，其中节点代表临床实体，边代表通过归一化点互信息识别的统计关联实体。在MIMIC-III和MIMIC-IV数据集上，MiGHT-EHR在药物推荐、住院时长预测、死亡率预测和再入院预测这四项任务的平均表现优于现有最优方法，尤其在死亡率和再入院预测方面提升显著。此外，对学习到的表示进行事后分析显示，患者邻域按临床结果组织，显著医学概念可作为表示空间中的线性方向被恢复，且任务概率校准良好。总体而言，这些发现表明MiGHT-EHR的表示支持多种预测任务，同时保留了可临床解释的结构。
英文摘要
Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction. However, effective learning remains challenging because EHRs encode heterogeneous, temporally ordered clinical interactions. In particular, EHRs contain: (i) heterogeneous clinical entities, including patients, visits, diagnoses, prescriptions, and procedures, together with their heterogeneous interactions, (ii) longitudinal patient trajectories across hospital visits and (iii) shared statistical dependencies across related clinical prediction tasks. Existing EHR learning methods capture only a subset of these properties. To bridge this gap, we propose Multi-task Graph transformer for Heterogeneous Temporal EHRs (MiGHT-EHR), which jointly models all three within a unified representation learning method. MiGHT-EHR constructs a heterogeneous graph from EHRs in which nodes represent clinical entities and edges connect statistically associated entities identified via normalized point-wise mutual information. Across MIMIC-III and MIMIC-IV datasets, MiGHT-EHR outperforms state-of-the-art methods on average across four tasks: drug recommendation, prediction of length-of-stay, mortality, and readmission, with particularly strong improvements in mortality and readmission prediction. Furthermore, a post-hoc analysis of the learned representations reveals that patient neighborhoods are organized by clinical outcomes, salient medical concepts are recoverable as linear directions in the representation space, and task probabilities are well calibrated. Collectively, these findings demonstrate that MiGHT-EHR representations support diverse prediction tasks while preserving clinically interpretable structure.
