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To develop a machine learning (ML) model to predict magnetic resonance imaging (MRI) appointment no-shows and test the effectiveness of a targeted phone call intervention in reducing the no-show rate.

To clarify how validation requirements should be specified for medical digital twins used in clinical decision support, particularly when such systems are intended to compare interventions, treatment timings, dosages, or sequential care strategies.

To evaluate the real-world performance of a transformer-based natural language processing (NLP) system for extracting Social Determinants of Health (SDoH) from clinical notes, using survey-based SDoH measures a reference comparators.

Accurately predicting outcomes for critically ill cancer patients remains challenging. This study aimed to integrate biologically relevant iron metabolism markers into short-term mortality prediction and to develop an interpretable, externally validated model based on a tabular prior-data fitted network (TabPFN) for risk stratification in this population.

Mental disorders are very common among children and adolescents around the world. In Mongolia, the international Strengths and Difficulties Questionnaire (SDQ) is used to detect mental disorders in adolescents. In this article, different types of classification methods were compared to determine adolescent emotional and behavioral problems using the SDQ.

We extracted a validated disease activity measure in rheumatoid arthritis (RA), the Clinical Disease Activity Index (CDAI), from a large tertiary academic medical center electronic health record (EHR) using an automated large language model (LLM)-based approach without requiring model pretraining.

Real-world data (RWD) have historically suffered from fragmentation, delayed availability, variable data quality, and limited analytic utility. Truveta developed an artificial intelligence (AI)-enabled data platform to address these longstanding challenges in using RWD for clinical research. This paper describes Truveta's partnership model, platform design, data scale, and research applications.

This study evaluated sequential multi-site fine-tuning of large language models (LLMs), simulating incremental deployment across institutions for extracting mobility functional status from unstructured clinical notes.

Treatment guidelines can improve population health; however, their implementation within electronic health records (EHRs) can be challenging. We aimed to create an implementable framework using the American Diabetes Association (ADA) Standards of Care (SOC) for people with type 2 diabetes and cardiovascular or renal disease as an example.

Building on innovations for autism detection-where artificial intelligence (AI)-based models monitor clinical data within electronic health records-this study evaluates the context for clinical decision support (CDS) deployment and identifies design preferences.