2 citations · 5 across the 7 of their papers we have counts for
7 papers
Transparent AI: Developing an Explainable Interface for Predicting Postoperative Complications
Yuanfang Ren, Chirayu Tripathi, Ziyuan Guan +10
Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public healt…
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…
The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)
Jessica Sena, Mohammad Tahsin Mostafiz, Jiaqing Zhang +9
Acuity assessments are vital in critical care settings to provide timely interventions and fair resource allocation. Traditional acuity scores rely on manual assessments and docume…
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
Yuanfang Ren, Yanjun Li, Tyler J. Loftus +9
Initial hours of hospital admission impact clinical trajectory, but early clinical decisions often suffer due to data paucity. With clustering analysis for vital signs within six h…