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.

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 ...

Sensor data from wearable devices analyzed over five years reveals walking and posture differences that predict fall risk in Parkinson’s patients. Study: Predicting future fallers in Parkinson’s ...

A Stanford-led study published in Nature on Feb. 26 found that age-related changes witnessed in diseases like Alzheimer’s may be related to a relatively untapped area of research in the brain. The ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

FIU Researchers are training AI to detect heart conditions, like aortic stenosis and heart failure, by analyzing heart sound data to improve early diagnosis and risk prediction. The future of heart ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...