AI RESEARCH
Capture Timing-Attention of Events in Clinical Time Series
arXiv CS.LG
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ArXi:2602.10385v2 Announce Type: replace Automatically discovering personalized sequential events from large-scale time-series data is crucial for enabling precision medicine in clinical research, yet it remains a formidable challenge even for contemporary AI models. For example, while transformers capture rich associations, they are mostly agnostic to event timing and ordering, thereby bypassing potential causal reasoning.