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Evidence record 161 · automatically gathered

X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models

Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model

Record details

Published: 7 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Healthcare · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (7 July 2026), “X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models,” evidence record 161, https://ethics.ai/record/161 (originally published by arXiv).

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