Information Technology

CardiacGPT™: A Real-Time AI Assistant for Intraoperative Guidance and Postoperative Decision Support in Cardiac Surgery

Cardiac surgery is one of the most complex and high-stakes areas of medicine, where intraoperative decisions must be made within seconds and incomplete information can compromise outcomes. Traditional risk scores and rule-based decision support tools provide limited real-time guidance and rarely integrate the unstructured data streams available during surgery. Recent advances in large language mod...

Predicting Hospital Admissions Using Pretrained EHR Embeddings: External Evaluation and Insights on Local Vocabulary Adaptation

Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice. This study evaluates whether publicly available pretrained transformer-based embeddings, developed on an external health system, can improve prediction of hospital admissions—including unplanned cases—when applied to a different institution with sp...

From Clinical Judgment to Large Language Models: Benchmarking Predictive Approaches for Unplanned Hospital Admissions

While machine learning (ML) models show strong performance for predicting unplanned hospital visits, their clinical utility relative to physician judg...

A modular pipeline for natural language processing-screened human abstraction of a pragmatic trial outcome from electronic health records

Natural language processing (NLP) allows efficient extraction of clinical variables and outcomes from electronic health records (EHR). However, measur...

Clinically Informed Semi-Supervised Learning Improves Disease Annotation and Equity from Electronic Health Records: A Glaucoma Case Study

Clinical notes represent a vast but underutilized source of information for disease characterization, whereas structured electronic health record (EHR...

Use of Generative AI for Health Among Urban Youth in Pakistan: A Mixed-Methods Study

GAI tools are increasingly used informally for health, yet evidence from low- and middle-income countries (LMICs) is limited. This study generates ear...

Privacy-Enhancing Sequential Learning under Heterogeneous Selection Bias in Multi-Site EHR Data

To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health ...

Accents Still Confuse AI: Systematic Errors in Speech Transcription and LLM-Based Remedies

Accurate and timely documentation in the electronic health record (EHR) is essential for delivering safe and effective patient care. AI-enabled medica...

Machine Learning Prediction of Pharmacogenetic Test Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: A Retrospective Cohort Study

Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due ...

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record Data

Information from electronic health records (EHRs) may be incorporated into computable phenotype algorithms in efforts to overcome inaccuracies of algo...

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model

Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across str...

A Self-Explainable Dynamic Risk Monitoring Framework for Predicting Alzheimer’s Disease and Related Dementias

Alzheimer’s Disease and Related Dementias (ADRD) affect millions worldwide and can begin over a decade before symptoms appear. ADRD are generally irre...

Forecasting left ventricular systolic dysfunction in heart failure with artificial intelligence

Objective assessment of left ventricular function remains a key prognosticator that is used to guide therapeutic decisions for patients with heart fai...

Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge

Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize re...

Predicting Behavioral Determinants of Health from Clinical Text Using Transformer Models and BiLSTM

Social and behavioral determinants of health play a critical role in patient outcomes, yet much of this information is documented only in unstructured...

Deep latent variable modelling reveals clinically significant subgroups among transfusion recipients

Transfusion recipients are a heterogeneous group of patients, yet the identification of these groups has traditionally relied on human-driven univaria...

Machine Learning for Dynamic and Short-term Prediction of Preeclampsia Using Routine Clinical and Laboratory Data

Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality, yet its unpredictable onset and rapid progression hinder timel...

Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting

Radiology residents require timely, personalized feedback to develop accurate image analysis and reporting skills. Increasing clinical workload often ...

TARGET-AI: a foundational approach for the targeted deployment of artificial intelligence electrocardiography in the electronic health record

Artificial intelligence (AI) applied to routine electrocardiograms (ECGs) offers promise for screening of structural heart disease (SHD), yet broad cl...

Development and automated deployment of a specialised machine learning schema within a collaborative research centre: an explorative approach using large language models

Achieving interoperability in machine learning (ML) workflows remains a significant challenge due to the heterogeneity of data types, algorithms, and ...