Explore JAMA Network Open’s Health Informatics collection, including open access science about electronic health records, approaches to Big Data, and more.
This diagnostic/prognostic study proposes a clinically applicable large-scale bidirectional generative adversarial network for predicting the efficacy of epidermal growth factor receptor–tyrosine kinase inhibitor therapy in patients with non–small cell lung cancer.
This randomized clinical trial examines the effect of a behavioral intervention email based on social norms feedback for physicians to reduce non–evidence-based prescriptions of nimodipine for cognitive impairment among older adults in Argentina.
This cohort study uses data clustering methods and clinical stakeholder assessment to identify clinical profiles in a population of medically complex patients.
This stepped-wedge cluster randomized clinical trial assesses the effect of a clinician-directed intervention combining machine learning mortality predictions with behavioral nudges vs usual care on motivating serious illness conversations between clinicians and patients with cancer.
This cohort study examines treatment effects of using a machine learning–derived treatment strategy vs treat all or treat none strategies using data from 4 randomized clinical trials among patients with septic shock.
This cohort study evaluates trends in Diagnosis Related Groups with a major complication or comorbidity and estimates associated changes in payment.
This prognostic study evaluates the concordance between blood pressures obtained in routine clinical practice and those obtained using the Systolic Blood Pressure Intervention Trial protocol and whether concordance varied by target trial blood pressure.
This Viewpoint examines the association between the COVID-19 pandemic and health care–related data collection.
This quality improvement study explores clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy.
This cohort study evaluates whether machine learning models could identify patients with intermediate-risk head and neck squamous cell carcinoma who would benefit from chemoradiation.
This Viewpoint proposes that Medicare claims data could inform the rollout of a COVID-19 vaccination program in the US, including notifying beneficiaries of their risk and prioritization and supporting efforts to allocate the vaccine and monitor its uptake, immunogenicity, and safety.
This cross-sectional study evaluates the use of machine learning for prediction of congenital adrenal hyperplasia based on distinct facial morphologic features.
This study compares pathogenic variant detection accuracy in cancer-disposition genes of prostate cancer and melanoma cohort biopsy samples using deep learning vs standard genetic analysis methods.
This cross-sectional study describes the development and evaluation of a deep learning model to identify allergic reaction in the free-text narrative of hospital safety reports.
This cohort study develops and validates deep learning models to understand public perceptions of human papillomavirus (HPV) vaccines from the perspective of behavior change theories using data from social media.
This diagnostic study evaluates whether the use of an artificial intelligence–based assistive tool is associated with improvements in the grading of prostate core needle biopsies among general pathologists compared with the majority opinion of subspecialists.
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