Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …

Medical applications of machine learning (ML) have experienced a surge in popularity in
recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of …

Accurately forecasting multiple future events within a given time horizon is crucial for
finance, retail, social networks, and healthcare applications. Event timing and labels are …

Effective, reliable, and scalable development of machine learning (ML) solutions for
structured electronic health record (EHR) data requires the ability to reliably generate high …

We introduce the Medical Event Data Standard (MEDS), a lightweight schema for enabling
machine learning over electronic health record (EHR) data. Unlike common data models …

Learning a decent representation from unlabeled time series is a challenging task,
especially when the time series data is derived from diverse channels at different sampling …

Reproducibility remains a significant challenge in machine learning (ML) for healthcare.
Datasets, model pipelines, and even task/cohort definitions are often private in this field …