BERT-LER模型在临床预测任务中表现出色，结合了实验室值表示和可解释性，适用于多个治疗领域和预测任务，值得一看。
BERT-LER模型通过将实验室测试结果编码为离散标记，并使用百分位数分组保留分级信息，结合集成梯度进行基于输入EHR序列的标记级归因，在EHRShot基准套件和哮喘严重程度进展研究中取得了与公开基准模型相媲美的预测性能。该模型在实验室相关任务上往往超过公开基准模型，并提供了与临床已知风险因素一致的归因。
Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records
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.