Time-to-Event Transformer to Capture Timing Attention of Events in EHR Time Series

• URL: http://arxiv.org/abs/2602.10385v1
• Date: Wed, 11 Feb 2026 00:13:08 GMT
• Title: Time-to-Event Transformer to Capture Timing Attention of Events in EHR Time Series
• Authors: Jia Li, Yu Hou, Rui Zhang,
• Abstract summary: LITT is a novel Timing-Transformer architecture that enables temporary alignment of sequential events on a virtual relative timeline''<n>Its interpretability and effectiveness are validated on real-world longitudinal EHR data from 3,276 breast cancer patients.
• Score: 15.049813932448112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Intuitively, we need a method capable of evaluating the "degree of alignment" among patient-specific trajectories and identifying their shared patterns, i.e., the significant events in a consistent sequence. This necessitates treating timing as a true \emph{computable} dimension, allowing models to assign ``relative timestamps'' to candidate events beyond their observed physical times. In this work, we introduce LITT, a novel Timing-Transformer architecture that enables temporary alignment of sequential events on a virtual ``relative timeline'', thereby enabling \emph{event-timing-focused attention} and personalized interpretations of clinical trajectories. Its interpretability and effectiveness are validated on real-world longitudinal EHR data from 3,276 breast cancer patients to predict the onset timing of cardiotoxicity-induced heart disease. Furthermore, LITT outperforms both the benchmark and state-of-the-art survival analysis methods on public datasets, positioning it as a significant step forward for precision medicine in clinical AI.

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