Article

An explainable transformer model learning from entire treatment timelines for pan-cancer risk profiling across healthcare systems

2026-07-27

Abstract excerpt

Cancer outcomes vary widely between individual patients, each accumulating an irregular record of treatments, diagnoses, measurements, and complications. Current prognostic models reduce this complexity into a single snapshot, focus on narrow clinical settings, and rarely generalize across hospitals. Here we introduce Chronicle , an explainable transformer that learns from entire patient trajectories to predict d...

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Literature Corpus work: eb6fe8a4-0f59-55cd-b1af-d665e616c645
DOI: 10.64898/2026.07.24.26358838

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