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 ...

Digestive system cancers, including hepatobiliary and gastrointestinal malignancies, remain a major global oncological burden ...

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...

Cedars-Sinai Health Sciences University investigators developed an AI-based model that can identify hospitalized patients at ...

Chronic wounds remain a major health care challenge, especially for people with diabetes, who often experience delayed ...

Chronic wounds remain a major healthcare challenge, especially for people with diabetes, who often experience delayed healing ...

Chronic diabetic wounds heal slowly and are highly vulnerable to infection. Researchers have developed an AI-guided, 4D-printed microneedle patch that actively closes wounds, fights bacteria, and ...

AI-designed microneedles bend at body temperature to close diabetic wounds while delivering DNA therapy and antibacterial ...

A proteomics- and machine learning (ML)-based precision prediction system enhances early risk stratification for diabetic retinal neurodegeneration (DRN), according to a study published online June 2 ...

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

Glucose tracking is moving beyond diabetes care as CGMs, AI platforms, and wearable sensors reshape personalized health data and wellness tools. Blood sugar is becoming the next number wellness ...