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
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- LinkedLinked via arxiv author · 85%Jun Ni Du →
“Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records”
- LinkedLinked via arxiv author · 85%Lukas Adamek →
“Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records”
- LinkedLinked via arxiv author · 85%Maxim Kryukov →
“Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records”
- LinkedLinked via arxiv author · 85%Flavio Dormont →
“Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records”
- LinkedLinked via arxiv author · 85%Ziv Bar-Joseph →
“Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records”
- LinkedLinked via arxiv author · 85%Sven Jager →
“Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records”