176 citations · 183 across the 12 of their papers we have counts for
12 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…
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…
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…
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…
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…
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…
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…