Information Technology

Automated Multisource Electronic Frailty Index in Acute Ischemic Stroke: Development and Clinical Utility

Background: Frailty is common in acute ischemic stroke (AIS) and predicts poor outcomes, but is not routinely captured in acute stroke care. Manual frailty tools are difficult to apply consistently in busy inpatient settings, while existing electronic frailty indices (eFIs) often rely on limited data modalities. We developed a scalable pre-stroke electronic frailty index (eFI) using multisource el...

Developing an OMOP-Standardized Prostate Cancer Database and Improving Data Quality Using NLP and PSA-Based Algorithms

Objective: To develop and evaluate an Observational Medical Outcomes Partnership (OMOP) standardized prostate cancer database from the University of Texas Medical Branch (UTMB) Epic Electronic Health Record (EHR) and improve data quality using natural language processing (NLP) and prostate-specific antigen (PSA) based algorithms. Materials and Methods: We built a data pipeline to transform UTMB Ep...

Fast and Accurate Anomaly Detection in Time Series

Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, ...

How Should Transformers Encode Numeric Values in Electronic Health Records?

How do we encode numeric values in transformer-based sequence processing, particularly in electronic health record (EHR) data? We systematically compa...

RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR

Background: Rare diseases affect a significant portion of the global population, yet patients often endure a lengthy diagnostic odyssey, frequently mi...

NLP Framework for Automated Symptom Severity Staging in Heart Failure and COPD Clinical Notes Using Ontology Integration: A Study Protocol

Background: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are among the leading causes of morbidity and mortality globally, with...

TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

The growing demand for privacy-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows. D...

PHO-Agents: A Large Language Model Powered Multi-Agent System for Predicting Health Outcomes

Objective: Predicting health outcomes from electronic health records (EHRs) is challenging because traditional models rely on structured data and ofte...

A language model framework for sequence modeling of EHR audit logs to characterize clinician-EHR interactions

Objective: Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated meas...

Leveraging Machine Learning Approaches to Identify Health-Related Social Needs Screening from Electronic Health Records

Health-related social needs (HRSNs), such as housing instability, food insecurity, and transportation challenges, are nonmedical factors associated wi...

Early identification of advanced chronicity (MACA) patients using Machine Learning models: a population-based predictive approach for proactive care stratification

Early identification of patients with advanced chronic conditions (MACA) remains a critical challenge in clinical practice, often relying on retrospec...

A next-generation electronic frailty index leveraging deep learning on unstructured health records extends risk prediction across the full frailty spectrum

Background: Existing electronic frailty indices (eFI) are typically based on structured data and designed for older adults. We developed an eFI that i...

Predicting Depression and Anxiety Progression in Multiple Sclerosis from Longitudinal Clinical Data Using Machine Learning

Depression and anxiety are highly prevalent in multiple sclerosis (MS), yet tools for predicting mental health trajectories from clinical data remain ...

Graph Neural Networks Applications Across Domains: All Insights You Need

Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. T...

PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models

Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot dir...

Artificial Intelligence-Enabled Cardiac Function Estimation from Phone Videos of Echocardiograms

Importance: Mobile phone-recorded echocardiogram videos are commonly used in point of care, telemedicine, and resource-limited workflows, but artifici...

Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification

Background: Guiding risk-appropriate inpatient thromboprophylaxis requires venous thromboembolism (VTE) risk stratification; however, reliable risk de...

Extraction of Glaucoma Diagnosis, Type, and Severity from Clinical Notes using Secure Cloud-based Large Language Models

Purpose: To evaluate the performance of secure cloud-based large language models (LLMs) in extracting glaucoma diagnosis, type, and severity from free...

Personalizing Suicide Risk Assessment: Machine Learning Extraction of Cross-Modal Interactions Between Psychosocial and Demographic Factors in Veterans

Background: Veterans face an elevated risk of suicide compared to the general population, motivating national efforts to develop predictive models tha...

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans

Background: Suicide remains a significant and potentially preventable cause of death among United States veterans. Predictive models based on structur...