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To discuss the origins of HL7 and its subsequent impact on interoperability in hospitals.

Fully automated digital interventions show promise for disseminating evidence-based strategies to manage insomnia complaints. However, an important concept often overlooked concerns the extent to which users adopt the recommendations provided in these programs into their daily lives. Our objectives were evaluating users' adherence to the behavioral recommendations provided by an app, and exploring whether users' perceptions of the app had an impact on their adherence behavior.

This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes.

To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations.

This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.

This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.

To describe real-world practices and variation in implementation of the Information Blocking provisions amongst healthcare organizations caring for pediatric patients.