A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.

Sepsis is one of the most common and lethal syndromes encountered in intensive care units (ICUs), and acute respiratory failure (ARF) represents one of its most critical complications. Once ...

A new study offers insight into the health and lifestyle indicators—including diet, physical activity and weight—that align most closely with healthy brain function across the lifespan. The study used ...

Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...

A study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported outcomes and wearable sensor data can predict which patients with ...

Researchers developed a new model to predict the likelihood of critical illness in patients with connective-tissue disease-associated ILD.

David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

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, ...

Local factors such as seasonal temperature, the year-dependent water and vegetation index, and data on animal density can be used to predict regional outbreaks of avian flu in Europe. This is the ...

Bayesian network models predict urgent care visits in patients with non–small cell lung cancer receiving systemic therapy. The first model (left) integrates clinical and patient-reported outcome data, ...