3 papers
cs.LG2026
One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models
Zilin Jing, Vincent Jeanselme, Yuta Kobayashi +6
Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory v…
cs.LG2026
A pipeline for enabling path-specific causal fairness in observational health data
Aparajita Kashyap, Sara Matijevic, Noémie Elhadad +2
When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare…
cs.LG2025
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
Chao Pang, Vincent Jeanselme, Young Sang Choi +9
Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (i…