Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

The framework predicts how proteins will function with several interacting mutations and finds combinations that work well ...

More than half of transplant recipients in a large analysis developed chronic graft-versus-host disease, and 15% died from causes other than cancer relapse. Those numbers capture the uneasy truth of ...

The severity of symptoms in posttraumatic stress disorder (PTSD) varies greatly across individuals in the first year after trauma and it remains difficult to predict whether someone might worsen, ...

A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve risk stratification.

Postpartum depression (PPD) affects up to 15 percent of individuals after childbirth. Early identification of patients at risk of PPD could improve proactive mental health support. Researchers ...

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?