5 papers
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…
Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Esra Adiyeke, Tianqi Liu, Venkata Sai Dheeraj Naganaboina +10
Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system generating treatment recommendations b…
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…
MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model
Miguel Contreras, Jessica Sena, Andrea Davidson +9
Acute brain dysfunction (ABD) is a common, severe ICU complication, presenting as delirium or coma and leading to prolonged stays, increased mortality, and cognitive decline. Tradi…
MANGO: Multimodal Acuity traNsformer for intelliGent ICU Outcomes
Jiaqing Zhang, Miguel Contreras, Sabyasachi Bandyopadhyay +9
Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have si…