Information Technology

Prospective Evaluation of AI Risk Stratification for Triaging Expedited Screening Mammogram Interpretation

To prospectively evaluate the feasibility and performance of expedited screening mammogram interpretation for women identified as high-risk by a deep learning risk model. This HIPAA-compliant, IRB-approved prospective controlled study was conducted at an urban safety-net facility. The Mirai breast cancer risk model was retrospectively validated on 114,229 local mammograms (2006–2023) to identify t...

The Application of Artificial Intelligence in Healthcare Practice: An Umbrella Review

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, I...

GERBEHRT: A BERT-based Model Tailored for German Electronic Health Records – Potential in Chronic Kidney Disease Prediction

Routinely collected electronic health records (EHRs) contain rich longitudinal information that enables the prediction of patient outcomes at scale. W...

Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort study

Accurate risk stratification in heart failure (HF) is crucial to guide clinical decisions, optimise therapeutic strategies and inform resource allocat...

Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records

Preterm birth is a syndrome that is triggered by diverse biological pathways and presents with many comorbid diseases. Although twin studies reveal a ...

Evaluating large language models for predicting psychiatric acute readmissions from clinical notes of population-based EHR

Psychiatric patients often have complex symptoms and anamneses recorded as unstructured clinical notes. Large language models (LLM) now enable large-s...

PANCDetect: Early Detection of Pancreatic Cancer from Multimodal EHR data with LLM Embeddings

Pancreatic cancer (PANC) is often diagnosed at late stages due to the absence of specific early symptoms, resulting in one of the highest cancer morta...

Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning

Dementia encompasses diverse clinical syndromes where diseases of the brain can manifest as impaired cognitive abilities, such as in Alzheimer’s disea...

PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI

Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not exp...

Inferring rheumatoid arthritis disease activity status from the electronic health records across health systems to enable real-world data studies

Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. However, RA disease activity is inconsistently recorded in real-w...

CLINPREAI: AN AGENTIC AI SYSTEM FOR EARLY POSTPARTUM DEPRESSION RISK PREDICTION FROM MULTIMODAL EHR DATA

Postpartum depression (PPD) affects 10–15% of mothers annually, yet early identification remains challenging. We introduce ClinPreAI, a novel agentic ...

SPELL: A Scalable NLP Method Using Regular Expressions and Large Language Models for Clinical Information Extraction

Electronic health records (EHRs) contain valuable information for clinical research and decision-making. However, leveraging these data remains challe...

Ambient Only vs. Longitudinal Data-Enhanced AI Documentation: A Pilot Study Quantifying the Value of Historical Clinical Context in Primary Care

Ambient artificial intelligence (AI) clinical documentation tools have gained rapid adoption in healthcare to address physician burnout from documenta...

Prediction of Long COVID and Mortality among Patients with Substance Use Disorder

The convergence of the COVID-19 pandemic and the substance use disorder (SUD) crisis has created a syndemic that places this vulnerable population at ...

Develop and Validate A Fair Machine Learning Model to Indentify Patients with High Care-Continuity in Electronic Health Records Data

Electronic health record (EHR) data often missed care outside a given health system, resulting in data discontinuity. We aimed to: (1) quantify miscla...

Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework

Obesity is a chronic, heterogeneous condition, with risks, trajectories, and treatment responses that vary widely among individuals. However, research...

Multimodal Electronic Health Record Foundation Models with Electrocardiogram for Cardiovascular Disease Prediction

Electronic health record (EHR) foundation models (FMs) have improved clinical task performance by learning comprehensive clinical context from sequent...

Physician- versus Large Language Model-Generated Clinical Summaries in the Emergency Department

As part of routine practice and documentation, emergency department (ED) clinicians routinely construct “one-liner” summaries—brief, information-rich ...

Predicting Alzheimer’s Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models

There is need to detect and intervene in pre-clinical phases of Alzheimer’s disease (AD). Electronic health records (EHRs) may help predict AD using m...

Build fair machine learning models to predict adverse outcomes for Heart failure patients with preserved ejection fraction (HFpEF) and with reduced ejection fraction (HFrEF)

Heart failure (HF), including heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), remains...