为临床时间序列设计个体化时间注意力模型，实现无先验的患者轨迹发现与反事实推断。
Capture Timing-Attention of Events in Clinical Time Series
- 引入个体时间注意力机制，将事件时间分布转化为可计算维度
- 在3276例乳腺癌患者数据上实现自动患者分型与反事实时间推断
- 无需领域知识，适用于个性化医疗与生存分析场景
当前轨迹学习范式主要基于群体动态，将个体复杂性简化为群体模型，导致有效患者分型困难且个体建模难以实现。本文提出一种数据驱动范式，引入专用的个体级时间变量以捕捉事件发生时间分布的集中程度（即时间注意力），使时间成为可计算维度，从而支持轨迹学习中的个体化时间特征。该方法以个体时间层级变换（LITT）形式实现，并应用于3,276名乳腺癌患者的纵向电子病历数据。据我们所知，首次实现了：(1) 自动发现具有临床意义的患者轨迹；(2) 反事实时间推断（即“如果-机器”）。两项结果均为纯数据驱动，无需先验领域知识。LITT在时间预测与生存分析任务中也表现优异。
原文摘要 · Abstract (English)
The contemporary paradigm of trajectory learning operates fundamentally at the level of group dynamics, systematically reducing individual-level complexity to fit group-level models, thus rendering effective patient subtyping difficult and individual-level modeling largely out of reach. We propose a data-driven paradigm that introduces a dedicated individual-level temporal variable to capture \emph{Timing Attention} (i.e., the degree of concentration of an event's timing distribution across the patient cohort), thereby rendering timing a \emph{computable dimension} that enables individualized temporal features in trajectory learning. Instantiated as the Level-of-Individual Time Transformation (LITT) and applied to longitudinal EHR data from 3,276 breast cancer patients, the proposed paradigm demonstrates, for the first time to our knowledge: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction, that is, a \emph{What-If Machine}. Both results are purely data-driven, requiring no prior domain knowledge. LITT further achieves strong performance on timing prediction and survival analysis tasks.
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