176 citations · 224 across the 39 of their papers we have counts for
40 papers
Teaching LLMs How ICU Physicians Approach Clinical Reasoning Through OMOP-Aligned Retrieval Improves Reasoning Across Clinical Domains
Miguel Contreras, Scott Siegel, Subhash Nerella +30
Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficult in the data-dense and rapidly…
A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction
Jessica Sena, Shesadree Priyadarshani, Miguel Contreras +4
Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally…
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…
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…
Human-Centered Development of an Explainable AI Framework for Real-Time Surgical Risk Surveillance
Andrea E Davidson, Jessica M Ray, Yulia Levites Strekalova +2
Background: Artificial Intelligence (AI) clinical decision support (CDS) systems have the potential to augment surgical risk assessments, but successful adoption depends on an unde…
An Iterative, User-Centered Design of a Clinical Decision Support System for Critical Care Assessments: Co-Design Sessions with ICU Clinical Providers
Andrea E. Davidson, Jessica M. Ray, Ayush K. Patel +3
This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driv…
Quantifying Circadian Desynchrony in ICU Patients and Its Association with Delirium
Yuanfang Ren, Andrea E. Davidson, Jiaqing Zhang +6
Background: Circadian desynchrony characterized by the misalignment between an individual's internal biological rhythms and external environmental cues, significantly affects vario…
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…
Epidemiology, Trajectories and Outcomes of Acute Kidney Injury Among Hospitalized Patients: A Retrospective Multicenter Large Cohort Study
Esra Adiyeke, Yuanfang Ren, Shmuel Fogel +5
Background: Acute kidney injury (AKI) is a clinical syndrome affecting almost one fifth of hospitalized patients, as well as more than half of the patients who are admitted to the…
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…
Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility
Scott Siegel, Jiaqing Zhang, Sabyasachi Bandyopadhyay +5
Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on h…
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…
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
Darren Liu, Cheng Ding, Delgersuren Bold +16
The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance. However, their performance in actual clinical ap…
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…