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To develop and validate a machine learning model that helps physician advisors efficiently identify hospital admission denials likely to be overturned on appeal.

The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.

In recent years, the rise of big data and artificial intelligence has led to an increasing expansion of databases and web services in biomedical research. cBioPortal is one of the most widely used platforms for accessing cancer genomic and clinical data. The primary objective of this study was to develop a tool that simplifies programmatic interaction with cBioPortal's web service.

The automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.

Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

Measurement of health-related social needs (HRSNs) is complex. We sought to develop and validate computable phenotypes (CPs) using structured electronic health record (EHR) data for food insecurity, housing instability, financial insecurity, transportation barriers, and a composite-type measure of these, using human-defined rule-based and machine learning (ML) classifier approaches.

To compare various methods for extracting daily dosage information from prescription signatures (sigs) and identify the best performers.

To be usable, useful, and sustainable for families of children with medically complex conditions (CMC), digital interventions must account for the complex sociotechnical context in which these families provide care. CMC experience higher neighborhood socioeconomic disadvantage than other child populations, which has associations with CMC health. Neighborhoods may influence the structure and function of the array of caregivers CMC depend upon (ie, the caregiving network).

This study evaluates the impact of an ambient artificial intelligence (AI) documentation platform on clinicians' perceptions of documentation workflow.

The lack of a publicly accessible abdominal X-ray (AXR) dataset has hindered necrotizing enterocolitis (NEC) research. While significant strides have been made in applying natural language processing (NLP) to radiology reports, most efforts have focused on chest radiology. Development of an accurate NLP model to identify features of NEC on abdominal radiograph can support efforts to improve diagnostic accuracy for this and other rare pediatric conditions.