Researchers at Penn State College of Medicine have developed a new artificial intelligence algorithm that may lead to improved predictions and novel therapies for autoimmune diseases. The algorithm ...

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 ...

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

A new machine learning model developed by The George Institute for Global Health can successfully predict heart disease risk in women by analyzing mammograms. The findings were published today in ...

Read more about From disease detection to biomass forecasting: AI improves aquaculture risk strategy on Devdiscourse ...

Acute hepatic porphyria (AHP) is a rare genetic disease with symptoms that overlap with many other conditions, making it extremely challenging to diagnose. Its symptoms mostly affect women with severe ...

In a recent study published in the journal Proceedings of the National Academy of Sciences, a team of scientists from China developed a multimodal method using an image Transformer system that uses ...

The predictive variables assessed were age at EGPA diagnosis, baseline eosinophil count, history of chronic sinusitis prior to diagnosis, and glucocorticoid-treated asthma at diagnosis.

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

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