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

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 machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a complication that can occur late in pregnancy. Preeclampsia is a sudden ...

Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial We trained and tested ML systems ...

Predicting earthquakes has long been an unattainable fantasy. Factors like odd animal behaviors that have historically been thought to forebode earthquakes are not supported by empirical evidence. As ...

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center demonstrated the ability to accurately predict responses to immunotherapy for ...

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 ...

Two complementary predictors (DAAE-M and ELIE) estimate individualized 5-year progression risk using routine clinical data, extending the prior DAAE framework beyond static baseline risk. Registry ...

UTokyo and Kubota develop a drone potato yield prediction method combining multispectral imagery, AI, and growth models.

Medicine has always operated as an “evidence based” field, meaning that it generally pursues experimentation to gather ...