5 papers · 1 filter
Federated generative event models for tokenized electronic health records
Michael C. Burkhart, Luke Solo, Inhyeok Lee +8
Electronic health record foundation models are limited by institutionally siloed data and substantial performance degradation under cross-site transfer. We evaluated federated trai…
Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
Sahil Sethi, David Chen, Michael C. Burkhart +3
Prototype-based neural networks offer interpretable predictions by comparing inputs to learned, representative signal patterns anchored in training data. While such models have sho…
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Sahil Sethi, David Chen, Thomas Statchen +4
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanatio…
Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models
Michael C. Burkhart, Bashar Ramadan, Luke Solo +2
We present a foundation model-derived method to identify highly informative tokens and events in electronic health records. Our approach considers incoming data in the entire conte…
Foundation models for electronic health records: representation dynamics and transferability
Michael C. Burkhart, Bashar Ramadan, Zewei Liao +4
Foundation models (FMs) trained on electronic health records (EHRs) have shown strong performance on a range of clinical prediction tasks. However, adapting these models to local h…