64 citations · 67 across the 7 of their papers we have counts for
5 papers · 1 filter
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 (…
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,…
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…
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series
Anna Wong, Shu Ge, Nassim Oufattole +4
Sepsis is a life-threatening condition triggered by an extreme infection response. Our objective is to forecast sepsis patient outcomes using their medical history and treatments,…