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.

翻译：当前轨迹学习的范式从根本上基于群体动态层面，系统性地将个体复杂性简化为适应群体模型，导致有效的患者亚型识别困难，且个体层面建模基本不可实现。我们提出一种数据驱动范式，通过引入专用个体级时间变量来捕捉"时序注意力"（即事件在患者队列中时间分布的集中程度），从而将时间转化为可计算的维度，为轨迹学习提供个体化时间特征。该范式以个体化时间变换（LITT）为实例，应用于3,276名乳腺癌患者的纵向电子健康档案数据后，首次（据我们所知）实现了：（1）自动发现具有临床意义的患者轨迹，以及（2）反事实时序推演——即"假设分析引擎"。两项结果均完全基于数据驱动，无需先验领域知识。LITT在时间预测与生存分析任务中亦展现出强劲性能。