Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...

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 ...

Explore how machine learning in insurance enhances risk assessment, fraud detection, and personalization. ✓ Subscribe for ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications after stem cell and bone marrow transplants, according to new research ...

Radiation therapy is a cornerstone of lung cancer treatment. But even when delivered with precision, radiation can damage healthy lung tissue. "Try as we might, when we deliver radiation to a cancer, ...

A recent study suggests that a freely available AI tool could help predict dangerous complications after stem cell transplants.

Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may underestimate risk in elderly East Asian patients. Researchers from Japan used ...

Predicting hospitalization risk among patients on hemodialysis is vital, given their high rate of unplanned hospitalizations related to fluid overload and infections.

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

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 ...