Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records

2026-08-20

AI Summary

该研究提出了一种名为BERT-LER的模型，通过将实验室测试结果编码为离散标记并结合积分梯度方法来提高电子健康记录上临床预测任务的可解释性。

Abstract

Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events. We present BERT-LER, a BERT-style model for coded EHR timelines pretrained and fine-tuned from a de-identified EHR dataset of 75 million patients, that encodes laboratory test results as discrete tokens while retaining graded information through percentile-based binning, paired with Integrated Gradients for token-level attributions grounded in the input EHR sequence. We benchmark our approach on the public EHRShot benchmark suite and on an asthma severity progression study based on real-world data. This addresses a methodological gap in EHR foundation-style modeling by unifying laboratory value representation and explainability in a single framework, while assessing whether both predictive performance and explanations generalize beyond standard clinical prediction tasks. Across EHRShot and asthma tasks, BERT-LER achieves predictive performance that is competitive with, and on laboratory-related tasks often exceeds, publicly available benchmark models, and provides attributions that align with clinically known risk factors. Our architecture and explainability approach can be applied to many therapeutic areas and prediction tasks using language models trained on structured EHRs.

Problem

• Explainability
• Clinical Prediction
• Electronic Health Records
• Laboratory Information

Innovation

• Explainable AI
• Transformers for EHRs
• Integrated Gradients
• Laboratory Value Encoding
• Clinical Predictions