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A point-of-care resource is required to support Registered Dietitian Nutritionists (RDNs) in providing comprehensive culturally-tailored nutrition services. This study aims to describe the iterative development and testing of a comprehensive nutrition assessment tool as part of a novel web-based platform to Support Personalized and Inclusive Cuisines in Environments for Healthcare (SPICE-Healthcare).

To evaluate the accuracy, computational cost, and portability of a new natural language processing (NLP) method for extracting medication information from clinical narratives.

While timely interventions can improve medication adherence, it is challenging to identify which patients are at risk of nonadherence at point-of-care. We aim to develop and validate flexible machine learning (ML) models to predict a continuous measure of adherence to guideline-directed medication therapies (GDMTs) for heart failure (HF).

Building upon our previous work on predicting treatment retention in medications for opioid use disorder, we aimed to improve 6-month retention prediction in buprenorphine-naloxone (BUP-NAL) therapy by incorporating features derived from large language models (LLMs) applied to unstructured clinical notes.

Breast diffusion-weighted imaging (DWI) has shown potential as a standalone imaging technique for certain indications, eg, supplemental screening of women with dense breasts. This study evaluates an artificial intelligence (AI)-powered computer-aided diagnosis (CAD) system for clinical interpretation and workload reduction in breast DWI.

The objective of this study is to evaluate retrieval-augmented prediction for forecasting hospital length of stay (LOS) following surgery compared to traditional machine learning (ML), standalone large language models (LLMs), and retrieval-augmented generation (RAG) approaches.

To address the challenges of data heterogeneity and manual feature engineering in clinical predictive modeling, we introduce FHIR-Former, an open-source framework integrating Fast Healthcare Interoperability Resources (FHIR) with large language models (LLMs) to automate and standardize clinical prediction tasks.

Acute otitis media (AOM) is a leading cause of pediatric antibiotic overuse. Safety Net Antibiotic Prescriptions (SNAPs) are recommended for antibiotic stewardship but are difficult to identify due to lack of structured documentation.

To assess whether HL7 Fast Healthcare Interoperability Resources (FHIR) can underpin a fully standards-based, end-to-end digital research architecture, demonstrate it in a live study, and quantify its benefits for interoperability and development efficiency.

Patients of different race have different outcomes following renal transplantation. Patients of different race also undergo renal transplantation at different hospitals. We used a novel decentralized multisite approach to quantitatively assess the effect of site of care on racial disparities between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in post-transplantation survival times.