6 papers
Rethinking How We Evaluate Methodological Progress in Health AI
Florent Pollet, Matthew McDermott
Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which condi…
SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
Zhuhan Bao, Rui Yang, Bohao Yang +16
Background. The widespread deployment of ambient digital scribes is driving large-scale capture of clinician-patient dialogues. Human coding of clinical communication data remains…
A Systematic Study of Biomedical Retrieval Pipeline Trade-offs in Performance and Efficiency
Hayk Stepanyan, Matthew McDermott
Retrieval systems are increasingly used in biomedical and clinical natural language processing applications, yet practical guidance for researchers building such systems is limited…
Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators
Luke Solo, Matthew B. A. McDermott, William F. Parker +3
Generative foundation models trained on tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction via Monte Carlo sampling of simulated future…
Beyond the Clinic: A Large-Scale Evaluation of Augmenting EHR with Wearable Data for Diverse Health Prediction
Will Ke Wang, Rui Yang, Chao Pang +7
Electronic health records (EHRs) provide a powerful basis for predicting the onset of health outcomes. Yet EHRs primarily capture in-clinic events and miss aspects of daily behavio…
Foundation Model of Electronic Medical Records for Adaptive Risk Estimation
Pawel Renc, Michal K. Grzeszczyk, Nassim Oufattole +9
Hospitals struggle to predict critical outcomes. Traditional early warning systems, like NEWS and MEWS, rely on static variables and fixed thresholds, limiting their adaptability,…