Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records
Ranking
Overall
79
Content
95
Popularity
41
Observed public metrics from 1 member.
Merged summary
TL;DR - BERT-LER is an explainable BERT-style model for structured EHR timelines, pretrained on de-identified records from 75 million patients. It combines percentile-binned laboratory values with token-level Integrated Gradients explanations, delivering competitive clinical predictions and clinically plausible attributions.
- Represents quantitative laboratory results as discrete tokens while preserving graded information through percentile-based binning.
- Evaluated on the public EHRShot benchmark and a real-world asthma severity progression study.
- Matches benchmark models overall and often outperforms them on laboratory-related tasks.
- Produces input-event attributions that align with established clinical risk factors.
Sources (1)
Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - BERT-LER is an explainable BERT-style model for structured EHR timelines, pretrained on de-identified records from 75 million patients. It combines percentile-binned laboratory values with token-level Integrated Gradients explanations, delivering competitive clinical predictions and clinically plausible attributions.
- Represents quantitative laboratory results as discrete tokens while preserving graded information through percentile-based binning.
- Evaluated on the public EHRShot benchmark and a real-world asthma severity progression study.
- Matches benchmark models overall and often outperforms them on laboratory-related tasks.
- Produces input-event attributions that align with established clinical risk factors.