Information Technology

Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression

Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood-level factors significantly influence PND risk, particularly among women of color, but current machine learning models using electronic medical records (EMRs) rarely incorporate neighborhood characteristics. To determine whether integrating neighbor...

Testing and Evaluation of Generative Large Language Models in Electronic Health Record Applications: A Systematic Review

The use of generative large language models (LLMs) with electronic health record (EHR) data is rapidly expanding to support clinical and research tasks. This systematic review synthesizes current strategies, challenges, and future directions for adapting and evaluating generative LLMs in EHR analyses and applications. We followed the PRISMA guidelines to conduct a systematic review of articles fro...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data

Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrim...

Interoperability of standardised electronic healthcare records facilitates transfer learning

Electronic healthcare records (EHR) use codes from different vocabularies to describe medical occurrences, often varying by type of care and country. ...

Artificial Intelligence for Early Detection and Prognosis Prediction of Diabetic Retinopathy

This review explores the transformative role of artificial intelligence (AI) in the early detection and prognosis prediction of diabetic retinopathy (...

A machine learning approach for automating review of a RxNorm medication mapping pipeline output

Medication mapping to standardized terminologies is an important prerequisite for performing analytics on a federated EHR network. TriNetX LLC operate...

Machine Learning Analysis of Electronic Health Records Identifies Interstitial Lung Disease and Predicts Mortality in Patients with Systemic Sclerosis

Interstitial lung disease (ILD) is the leading cause of death in patients with systemic sclerosis (SSc), affecting more than 40% of this population. D...

Domain Adaptation Strategies for Transformer-Based Disease Prediction Using Electronic Health Records

Electronic Health Records (EHRs) offer rich data for machine learning, but model generalizability across institutions is hindered by statistical and c...

Semantic Encoding in Medical LLMs for Vocabulary Standardisation

High-quality, standardised medical data availability remains a bot-tleneck for digital health and AI model development. A major hurdle is translating ...

Gaps in Artificial Intelligence Research for Rural Health in the United States: A Scoping Review

Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers globally. The current focus on AI deployments in urban area...

Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development

Myasthenia gravis (MG) is a rare autoimmune neuromuscular disease. Clinical trials with rigorously collected data, especially for rare diseases, provi...

Early Warning Model for Patient Deterioration: A Machine Learning Approach for Nurse-Led Monitoring

The early recognition of clinical deterioration in hospital inpatients continues to be a major challenge in healthcare. In this work, we proposed an i...

Artificial Intelligence Enabled Phenogrouping of Heart Failure with Preserved Ejection Fraction Depicts Early and End-Stage Trajectories

Heart failure with preserved ejection fraction is challenging to diagnose, precluding the initiation of prognostic medications. A deeper understanding...

Automated Insomnia Phenotyping from Electronic Health Records: Leveraging Large Language Models to Decode Clinical Narratives

Insomnia is a highly prevalent but often underdiagnosed condition in clinical practice. Its inconsistent documentation in electronic health records (E...

Enhancing Cause of Death Prediction: Development and Validation of ML Models Using Multimodal Data Across Multiple Healthcare Sites

Timely and accurate determination of causes of death (CoD) is essential for public health surveillance, epidemiological research, and healthcare polic...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Automatic ICD coding using LLMs: a systematic review

Manual assignment of International Classification of Diseases (ICD) codes is error-prone. Transformer-based large language models (LLMs) have been pro...

Zero-Shot Large Language Models for Long Clinical Text Summarization with Temporal Reasoning

Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajecto...

Development of an AI-enabled predictive model to identify the ‘sick child’ at a pediatric telemedicine and medication delivery service in Haiti

One of the most difficult challenges in pediatric telemedicine is to accurately discriminate between the ‘sick’ and ‘not sick’ child, especially in re...

Privacy Protection for Chinese Electronic Medical Records Using Large Language Models: Effectiveness Evaluation and Application of LLM Models in Medical Data Tasks

The privacy protection of medical patients has remained a critical concern in healthcare information management during the digital era. Conventional a...