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The primary objective of this research is to assess the content coverage of nursing data within a publicly available common data model (CDM), focusing on how nursing data, documented in flowsheets, are represented within the model.

The objective of this study was to develop and test natural language processing (NLP) methods for screening and, ultimately, predicting the cancer relevance of peer-reviewed publications.

To develop and evaluate an automated classification system for labeling Exposure Process Coding System (EPCS) quality codes-specifically exposure and encourage events-during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques.

Neonatal jaundice monitoring is resource-intensive. Existing artificial intelligence methods use image or clinical data, but none systematically combine both or compare feature contributions. This study fills that gap by extracting and analyzing multimodal features on a large dataset, identifying an optimal feature set for accurate, accessible jaundice assessment.

Prediction models using statistical or machine learning (ML) approaches can enhance clinical decision support tools. Infliximab (IFX), a biologic with a newly introduced biosimilar for Crohn's disease (CD) and ulcerative colitis (UC), presents an opportunity to evaluate these tools at time of biosimilar switch to predict disease flares. This study sought to evaluate real-world safety and effectiveness of nonmedical IFX biosimilar switch in a national US cohort of CD and [...]

Bias evaluations of machine learning (ML) models often focus on performance in research settings, with limited assessment of downstream bias following clinical deployment. The objective of this study was to evaluate whether CHARTwatch, a real-time ML early warning system for inpatient deterioration, demonstrated algorithmic bias in model performance, or produced disparities in care processes, and outcomes across patient sociodemographic groups.

The NIH's Bridge2AI Program has funded 4 "new flagship biomedical and behavioral datasets that are properly documented and ready for use with AI [artificial intelligence] or ML [machine learning] technologies" to promote the adoption of AI. This article discusses the challenges and lessons learned in data collection and governance to ensure their responsible use.

This study aims to develop and validate of a span-based annotation framework for clinical named entity recognition (NER) using large language models (LLMs) based on Korean emergency department clinical notes.

Identifying social determinants of mental health (SDOMH) in patients with opioid use disorder (OUD) is crucial for estimating risk and enabling early intervention. Extracting such data from unstructured clinical notes is challenging due to annotation complexity and requires advanced natural language processing (NLP) techniques. We propose the Human-in-the-Loop Large Language Model Interaction for Annotation (HLLIA) framework, combined with a Multilevel Hierarchical Clinical-Longformer Embedding (MHCLE) algorithm, to annotate and predict SDOMH [...]

This study describes the utilization and experiences of artificial intelligence (AI)-generated draft responses to patient messages in pediatric ambulatory clinicians and contextualizes their experiences in relation to those of adult specialty clinicians.