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This study aimed to identify and quantify semantic drift (ie, the change in semantic meaning over time) within expert-defined anxiety-related (AR) terminology and compare it to common electronic health record (EHR) vocabulary across longitudinal pediatric clinical notes.

To develop a machine learning method that estimates future liver biomarkers' values from longitudinal lifestyle (diet, activity) data for early detection of nonalcoholic steatohepatitis (NASH).

Global immunization efforts still face major inequities and declining vaccine confidence, leaving millions of children in low- and middle-income countries unvaccinated or under-vaccinated.

This study aims to apply 2 decoder-based Generative Pre-trained Transformer (GPT) models (GPT-4o and GPT-o3-mini) in automating the methodological appraisal of randomized controlled trials (RCTs), under a variety of prompt designs, and to compare their performance to a fine-tuned encoder-only BioLinkBERT model.

To develop recommendations to inform the development and use of pragmatic workflow approaches.

To simulate shortcut learning mechanisms in AI-based breast cancer genomic subtyping and to develop an interpretable, reproducible framework capable of auditing feature over-reliance using a low-code environment.

The accurate identification of Emergency Department (ED) encounters involving opioid misuse is critical for health services, research, and surveillance. We sought to develop natural language processing (NLP)-based models for the detection of ED encounters involving opioid misuse.

To explore the impact, barriers, and facilitators of routinely sharing clinic visit recordings with patients in diverse clinical settings.

Stigmatizing language in clinical documentation can contribute to healthcare disparities and affect patient-provider relationships. Given their strong capacity for contextual language understanding, large language models (LLMs) offer potential for detecting and reducing such language. This study evaluates the accuracy of LLMs in detecting stigmatizing language, focusing on model size, temperature settings, and the inclusion of examples.

Federated research networks, like Evolve to Next-Gen Accrual of patients to Clinical Trials (ENACT), aim to facilitate medical research by exchanging electronic health record (EHR) data. However, poor data quality can hinder this goal. While networks typically set guidelines and standards to address this problem, we developed an organically evolving, data-centric method using patient counts to identify data quality issues, applicable even to sites not yet in the network.