Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

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 ...

In 2024, CU Boulder identified the need for an intensive cross-campus focus on creating and assessing program learning outcomes to support student success, guide academic planning and meet the ...

Multimodal Artificial Intelligence Model From Baseline Histopathology Adds Prognostic Information for Distant Recurrence Assessment in Hormone Receptor–Positive/Human Epidermal Growth Factor Receptor ...

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 ...

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 ...

ABSTRACT: Background: Artificial intelligence (AI) technologies, including machine learning, natural language processing, and decision-support systems, are increasingly explored in primary care to ...

Intensive care unit–acquired bloodstream infections (ICU-BSIs) are among the most prevalent healthcare-associated infections and a major cause of mortality among ICU patients. We developed a machine ...

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 ...