Capture Timing-Attention of Events in Clinical Time Series 事件

Capture Timing-Attention of Events in Clinical Time Series arXiv:2602.10385v4 Announce Type: replace-cross Abstract: Automatically discovering personalized trajectories (i.e., sequential event patterns) from longitudinal EHR data is crucial for enabling precision medicine in clinical research, yet it remains a formidable challenge even for contemporary AI models. For example, while the attention mechanism of transformers can capture rich associations, it is largely agnostic to event timing and