A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.

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 ...

Adverse neighborhood conditions in early adulthood may raise the risk of early cardiovascular disease decades later.

Abstract: Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve patient outcomes. This paper presents a Heart Disease Prediction System ...

Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk ...

A major clinical trial involving 50 hospital intensive care units (ICUs) throughout New Zealand and Australia will test if ...

Scientists have identified a blood-based protein signature that can predict lung cancer risk more than five years before diagnosis. Powered by AI and validated across global datasets, the breakthrough ...

Mammograms, which are key to detecting breast cancer, could be paired with artificial intelligence to predict heart disease risk, too. Researchers have developed an AI model that scans mammograms to ...

A tool developed by the American Heart Association (AHA), proven to accurately predict heart disease risk for Americans, can be applied to the global population, a new study led by NYU Langone Health ...

Two complementary predictors (DAAE-M and ELIE) estimate individualized 5-year progression risk using routine clinical data, extending the prior DAAE framework beyond static baseline risk. Registry ...

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