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 on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while p
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- Linked via arxiv authorJie Huang →
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Model
- Linked via arxiv authorPengfei Yin →
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Model
- Linked via arxiv authorZihan Xu →
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Model
- Linked via arxiv authorDaniel Capurro →
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Model
- Linked via arxiv authorMike Conway →
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Model
- Linked via arxiv authorTing Dang →
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Model