A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

A novel multi-task XGBoost model shows robust overall performance in predicting antimicrobial resistance in common gram-negative pathogens.

A deep learning model using retinal images obtained during ROP screening may be used to predict diagnosis of BPD and PH.

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Recent study reveals machine learning's potential in predicting the strength of carbonated recycled concrete, paving the way ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Scientists have created an AI model that forecasts moderate heat stress—a major precursor to coral bleaching—at sites along ...

Researchers develop an AI tool to predict cardiometabolic multimorbidity risk in type 2 diabetes, aiding early intervention and personalised care. Find out more.

The UNLV Runnin' Rebels could knock off the Fresno State Bulldogs tonight, but it won't be easy.

Scientists have created an AI model that forecasts moderate heat stress — a major precursor to coral bleaching — at sites ...