32 citations · 42 across the 5 of their papers we have counts for
5 papers
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…
CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records
Chao Pang, Jiheum Park, Xinzhuo Jiang +5
Electronic Health Records (EHRs) provide a rich, longitudinal view of patient health and hold significant potential for advancing clinical decision support, risk prediction, and da…
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
Vincent Jeanselme, Zilin Jing, Aparajita Kashyap +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…
CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
Chao Pang, Xinzhuo Jiang, Nishanth Parameshwar Pavinkurve +8
Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without dire…
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
Chao Pang, Xinzhuo Jiang, Krishna S Kalluri +4
Embedding algorithms are increasingly used to represent clinical concepts in healthcare for improving machine learning tasks such as clinical phenotyping and disease prediction. Re…