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 ...

Global Digital Biomarkers Market OverviewThe global digital biomarkers market is poised for rapid expansion and is expected to grow at an impressive rate of nearly 20 percent over the next five years.

A research team from Juntendo University in Japan wanted to find a better way to predict survival for older people with heart failure. The project was led by Professor Tetsuya Takahashi, Assistant ...

WPI researchers use machine learning and brain scans to identify age- and sex-specific anatomical patterns that predict ...

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 ...

A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

Tiny RNA molecules carried by extracellular vesicles in the bloodstream can accurately predict kidney function decline and cardiovascular risk in chronic kidney disease (CKD), as reported by ...

Mammograms can detect the presence of breast arterial calcification, which is associated with a higher risk of future heart attacks and stroke. Mammograms detect tumors, but they can also identify ...

Discover how AI is transforming nutritional science by turning complex diet and omics data into predictive tools that reshape chronic disease prevention and personalized care.

Juntendo University researchers have trained a machine learning algorithm to use clinical information and physical function metrics to accurately estimate long-term survival odds of elderly patients ...