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Artificial intelligence (AI) can be used to measure sentiment and reading level, facilitating its use to improve nutrition communication, though AI has not yet been tested for such analysis of nutrition articles. The present study investigated this by looking at reading level and sentiment in magazines aimed at various demographics as determined with ChatGPT.

To produce the first structured longitudinal dataset of symptom severity trajectories in vagus nerve stimulation (VNS) patients using a 2-stage large language model pipeline for automated symptom severity extraction from clinical notes, as part of the NIH-funded research evaluating vagal excitations and anatomical linkages (U54AT012307).

Prescription drug monitoring programs (PDMPs) offer a promising data source for the development of clinical decision support (CDS) systems to guide opioid prescribing, but most PDMP agencies lack capacity for in-house model development-instead relying on third-party tools. This study aims to externally validate a PDMP-based model, developed using 2018 California PDMP records, to predict new long-term use among Kentucky opioid-naïve patients and compare its performance to Kentucky-specific machine learning (ML) [...]

To evaluate the feasibility and applicability of the studyathon format within a pharmaceutical company setting for generating reliable real-world evidence rapidly for a business-relevant research question.

Transformer-based models for clinical prediction using longitudinal electronic health record (EHR) data are often developed with large feature sets and long patient histories under the assumption that more data improves performance. However, high-dimensional inputs and long sequences increase computational burden, potentially limiting scalability. We evaluated how feature dimensionality and sequence length affect predictive performance, calibration, risk stratification, and computational efficiency in EHR prediction.

To develop and validate a weighting framework to improve representativeness of Oracle Health Real-World Data (OHRWD).

This study aimed to develop large language models (LLMs) to automatically identify antiretroviral therapy (ART) adherence barriers and stratify nonadherence risk levels from patient-generated messages.

Characteristics of electronic health record (EHR) use associated with burnout in inpatient pediatric settings remain unclear. This study aims to (1) measure the relationship between subjective EHR experience and burnout and (2) identify objective EHR- and schedule-based measures associated with EHR experience and burnout.

We examined the intention of lung cancer patients to use virtual reality (VR) to support shared decision-making and treatment preparedness and identified predictors of this intention using the Unified Theory of Acceptance and Use of Technology (UTAUT).

This study evaluates the quality of estimated delivery dates (EDD) captured in electronic health record (EHR) systems, focusing on identifying and quantifying bias in these estimates for patients seeking prenatal care.