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 ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...

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

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

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 ...

Zimmer said SynTuition matched expert’s PJI diagnosis 96% of the time, outperforming pooled physicians who matched experts 91% of the time.

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...

Machine learning, a type of artificial intelligence, has many applications in science, from finding gravitational lenses in the distant universe to predicting virus evolution. Hubble Space Telescope ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...