6 citations · 16 across the 18 of their papers we have counts for
8 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…
Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization
Inhyeok Lee, Luke Solo, Michael C. Burkhart +5
Generative medical event models use tokenized sequences of patient timelines as input, but practical guidance on the many decisions around tokenization is limited. We benchmark qua…
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…
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…
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…
Safe AI for health and beyond -- Monitoring to transform a health service
Mahed Abroshan, Michael Burkhart, Oscar Giles +7
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems oft…
Deep Low-Density Separation for Semi-Supervised Classification
Michael C. Burkhart, Kyle Shan
Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create…