176 citations · 197 across the 18 of their papers we have counts for
18 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…
Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring
Jiaqing Zhang, Sandeep Elluri, Bhanu Cherukuvada +7
Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal clinical scales remains poorly…
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…
Enhancing EHR Systems with data from wearables: An end-to-end Solution for monitoring post-Surgical Symptoms in older adults
Heng Sun, Sai Manoj Jalam, Havish Kodali +7
Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited…
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…
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…
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…
Transformers in Healthcare: A Survey
Subhash Nerella, Sabyasachi Bandyopadhyay, Jiaqing Zhang +9
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly…
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…
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…
End-to-End Machine Learning Framework for Facial AU Detection in Intensive Care Units
Subhash Nerella, Kia Khezeli, Andrea Davidson +3
Pain is a common occurrence among patients admitted to Intensive Care Units. Pain assessment in ICU patients still remains a challenge for clinicians and ICU staff, specifically in…
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture
Mizuki Miyatake, Subhash Nerella, David Simpson +4
Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images…
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning
Mamoun T. Mardini, Subhash Nerella Amal A. Wanigatunga, Santiago Saldana +2
Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-w…
Facial Action Unit Detection on ICU Data for Pain Assessment
Subhash Nerella, Azra Bihorac, Patrick Tighe +1
Current day pain assessment methods rely on patient self-report or by an observer like the Intensive Care Unit (ICU) nurses. Patient self-report is subjective to the individual and…