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 ...

A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant measurements and environmental data. By training models on seven years of ...

Effect of incorporating symptom burden with mortality as a composite outcome on accuracy and bias in palliative care identification algorithms in oncology. This is an ASCO Meeting Abstract from the ...

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 ...

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 ...

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

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 ...

A recent study published in npj Materials Degradation introduces a two-stage machine learning (ML) framework that predicts the degradation of protective coatings under various environmental conditions ...

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 ...