AToP: An Explainability Framework for Transformer-Based EHR Predictions Using Attribution-Guided Temporal Pattern Mining

Objective: To develop and evaluate AToP (Attribution to Temporal Patterns), a post-hoc explainability framework that converts per-token Integrated Gradients (IG) from a Transformer into structured temporal pattern narratives. Materials and Methods: AToP was applied to a single-stream Transformer trained on MIMIC-IV v3.1 (218,196 patients; 12.6% unplanned 30-day readmission). It mines frequent episodes from salient-token subsequences in the training cohort, aggregates attribution among patients whose records contain each token or pattern, and collapses near-duplicates by patient overlap. Valida

This content is for informational purposes only and is not medical advice. It is not intended to diagnose, treat, cure, or prevent any disease. Consult a healthcare professional before starting any supplement, treatment, or program: especially if you are pregnant, nursing, taking medication, or managing a health condition.