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.

UC Berkeley researchers say a new AI model found a hidden ECG signal that could help doctors identify more people at risk ...

Engineers have developed a new way to monitor how tiny lab-grown human heart tissues beat—by effectively "listening" to the ...

OCHIN researchers describe project designed to improve risk estimates by combining clinical information with patients’ lived ...

University of Pittsburgh postdoctoral researcher Mary Cundiff uses machine learning and single-cell genomics to study ...

The technology analyses complex multi-organ imaging and clinical data to identify disease severity, detect previously unknown ...

Blood proteins can reveal the biological age of different cell types. Accelerated cellular aging was linked to a higher risk of Alzheimer's disease, cancer, and premature death.

Sportschosun Jang Jong-ho] A study has found that changes in gut microbes may help predict the stage and prognosis of liver ...

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 ...

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 ...