5 papers
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,…
Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment
Pawel Renc, Michal K. Grzeszczyk, Linglong Qian +3
We present Federated Timeline Synthesis (FTS), a novel framework for training generative foundation models across distributed timeseries data applied to electronic health records (…
LEMoN: Label Error Detection using Multimodal Neighbors
Haoran Zhang, Aparna Balagopalan, Nassim Oufattole +4
Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the…
MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets
Nassim Oufattole, Teya Bergamaschi, Aleksia Kolo +4
Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-q…
Event-Based Contrastive Learning for Medical Time Series
Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott +4
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse o…