Six machine learning algorithms—k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, support vector machine, and Extreme Gradient Boosting (XGBoost)—were developed using 10-fold ...

Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...

Risk prediction of cardiac death following percutaneous coronary intervention remains suboptimal in acute myocardial infarction. This study aimed to develop and externally validate an interpretable ...

Machine learning can synthesize routinely collected demographic, laboratory, medical history, and lifestyle data to deliver noninvasive, individualized risk assessments that identify high-risk ...

Copyright: © 2026 The Author(s). Published by Elsevier Ltd. Clinical and laboratory data were collected in two independent, prospective cohort studies, including ...

Automated tumor content analysis from whole slide images using deep learning models for NGS quality control in precision oncology. Discrimination performance on temporal validation set (2017–2022).

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Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

Digital Mental Health Interventions for Aged Adults With Psychiatric Conditions ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

Background Multiple clinical risk scores have been developed for acute upper gastrointestinal bleeding (AUGIB) with limited sensitivity and specificity. We aim to develop a machine learning model to ...

aInstitute of Computer-Assisted Cardiovascular Medicine, Deutsches Herzzentrum der Charité Augustenburger Platz 1, 13353 Berlin, Germany bDepartment of Congenital Heart Disease – Pediatric Cardiology, ...