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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.06163v1

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Source

Plain-Language Brief

The authors propose a new approach to make Electronic Health Records Foundation Models (FEMRs) more transparent by identifying the most influential tokens in the model's predictions. This is done using a Transformer-based surrogate model that approximates the behavior of FEMRs while preserving temporal dynamics. The alignment between the surrogate model's key tokens and clinically validated features is used to evaluate the clinical relevance.

What the Document Says

  • The text discusses a proposal for a token-level explainability approach for Electronic Health Records Foundation Models (FEMRs).
  • The approach involves training a Transformer-based surrogate model to approximate the behavior of FEMRs across two prediction tasks, preserving temporal dynamics.
  • The most influential tokens are identified to provide insights into how FEMRs use patient history for predictions.
  • A clinical alignment metric is introduced to quantify the correspondence between the surrogate model's key tokens and clinically validated features.
  • Results show that the surrogate model closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge.

Key Concepts Mentioned

  • Electronic Health Records Foundation Models (FEMRs)
  • Transformer-based models
  • token-level explainability
  • clinical prediction tasks
  • bias
  • interpretability
  • clinical trust
  • patient history
  • surrogate model
  • temporal dynamics

Claims Made by the Document

  • The proposed approach provides insights into how FEMRs leverage different aspects of patient history for predictions.

Evidence Used

  • The text states that the most influential tokens are identified to provide insights into how FEMRs use patient history for predictions.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical alignment metric that quantifies the correspondence between the surrogate model's key tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.