15 citations · 47 across the 35 of their papers we have counts for
15 papers · 1 filter
Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information
Jiaqing Zhang, Sabyasachi Bandyopadhyay, Miguel Contreras +8
Delirium is a common and serious complication in the Intensive Care Unit (ICU), associated with increased morbidity, prolonged hospital stays, and higher healthcare costs. Despite…
Federated Learning with Multi-Partner OneFlorida+ Consortium Data for Predicting Major Postoperative Complications
Yuanfang Ren, Varun Sai Vemuri, Zhenhong Hu +6
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a large multicenter dataset from…
MELON: Multimodal Mixture-of-Experts with Spectral-Temporal Fusion for Long-Term Mobility Estimation in Critical Care
Jiaqing Zhang, Miguel Contreras, Jessica Sena +8
Patient mobility monitoring in intensive care is critical for ensuring timely interventions and improving clinical outcomes. While accelerometry-based sensor data are widely adopte…
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision
Yingbo Ma, Suraj Kolla, Zhenhong Hu +11
Modern electronic health records (EHRs) hold immense promise in tracking personalized patient health trajectories through sequential deep learning, owing to their extensive breadth…
Federated learning model for predicting major postoperative complications
Yonggi Park, Yuanfang Ren, Benjamin Shickel +8
Background: The accurate prediction of postoperative complication risk using Electronic Health Records (EHR) and artificial intelligence shows great potential. Training a robust ar…
Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records
Yingbo Ma, Suraj Kolla, Dhruv Kaliraman +10
The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health traj…