Article

Time-Aware Contrastive Transformer for Longitudinal Patient Representation Learning

2026-06-25

Abstract excerpt

Learning high-quality longitudinal patient representations from irregular electronic health records (EHRs) is essential for understanding heterogeneity in time-evolving diseases such as cancer. Longitudinal patient representation learning methods often rely on external labels for downstream tasks or do not model the temporal dynamics between medical events explicitly, reducing the clinical applicability of learned...

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Literature Corpus work: e7ca6914-33ac-5f5e-8ebc-c45d436913d1
DOI: 10.64898/2026.06.23.26356236

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