15 citations · 35 across the 24 of their papers we have counts for
24 papers
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…
Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Yuanfang Ren, Esra Adiyeke, Ziyuan Guan +6
Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of th…
Unlocking Health Insights with SDoH Data: A Comprehensive Open-Access Database and SDoH-EHR Linkage Tool
Zhenhong Hu, Esra Adiyeke, Ziyuan Guan +8
Background: Social determinants of health (SDoH) play a crucial role in influencing health outcomes, accounting for nearly 50% of modifiable health factors and bringing to light cr…
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…
DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR
Miguel Contreras, Sumit Kapoor, Jiaqing Zhang +7
Delirium is an acute confusional state that has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection of this condition could lead to more ti…
Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools
Yingbo Ma, Yukyeong Song, Jeremy A. Balch +12
As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical A…
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…
A multi-cohort study on prediction of acute brain dysfunction states using selective state space models
Brandon Silva, Miguel Contreras, Sabyasachi Bandyopadhyay +8
Assessing acute brain dysfunction (ABD), including delirium and coma in the intensive care unit (ICU), is a critical challenge due to its prevalence and severe implications for pat…
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
Esra Adiyeke, Yuanfang Ren, Benjamin Shickel +12
Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney…
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
Miguel Contreras, Brandon Silva, Benjamin Shickel +9
The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable. Early detection of deteriorating conditions can result in providing timely…
Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status
Subhash Nerella, Ziyuan Guan, Andrea Davidson +6
Intensive Care Units (ICU) provide close supervision and continuous care to patients with life-threatening conditions. However, continuous patient assessment in the ICU is still li…
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…
Transformer Models for Acute Brain Dysfunction Prediction
Brandon Silva, Miguel Contreras, Tezcan Ozrazgat Baslanti +5
Acute brain dysfunctions (ABD), which include coma and delirium, are prevalent in the ICU, especially among older patients. The current approach in manual assessment of ABD by care…
Predicting risk of delirium from ambient noise and light information in the ICU
Sabyasachi Bandyopadhyay, Ahna Cecil, Jessica Sena +8
Existing Intensive Care Unit (ICU) delirium prediction models do not consider environmental factors despite strong evidence of their influence on delirium. This study reports the f…
Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit
Yuanfang Ren, Tyler J. Loftus, Ziyuan Guan +7
In the United States, more than 5 million patients are admitted annually to ICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain dysfunction status, delirium…
Clinical Courses of Acute Kidney Injury in Hospitalized Patients: A Multistate Analysis
Esra Adiyeke, Yuanfang Ren, Ziyuan Guan +4
Objectives: We aim to quantify longitudinal acute kidney injury (AKI) trajectories and to describe transitions through progressing and recovery states and outcomes among hospitaliz…
AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing
Subhash Nerella, Ziyuan Guan, Scott Siegel +5
The intensive care unit (ICU) is a specialized hospital space where critically ill patients receive intensive care and monitoring. Comprehensive monitoring is imperative in assessi…
Application of Deep Interpolation Network for Clustering of Physiologic Time Series
Yanjun Li, Yuanfang Ren, Tyler J. Loftus +7
Background: During the early stages of hospital admission, clinicians must use limited information to make diagnostic and treatment decisions as patient acuity evolves. However, it…