AI systems are beginning to build and improve themselves. But without a verification layer, trust, safety and accountability ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
New academies unify hands-on learning and job-relevant assessments, helping organizations move from measuring course completion to proof of skills in a single experience Learn Academies integrate ...
Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...
Multimodal Artificial Intelligence Model From Baseline Histopathology Adds Prognostic Information for Distant Recurrence Assessment in Hormone Receptor–Positive/Human Epidermal Growth Factor Receptor ...
Learn how to choose the right cross-validation method for your machine learning projects. Compare techniques like k-fold, stratified, and leave-one-out cross-validation, and understand when to use ...
K-Fold cross-validation is popular, but it’s not always the best choice. Learn when K-Fold works, when it can mislead your results, and explore alternative validation strategies for more reliable ...
ABSTRACT: Background: Artificial intelligence (AI) technologies, including machine learning, natural language processing, and decision-support systems, are increasingly explored in primary care to ...
Radiation dermatitis (RD), a common adverse reaction in breast cancer radiotherapy, impairs quality of life and increases healthcare burdens. Developing an effective risk prediction model is crucial ...
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