Machine learning model predicts serious transplant complications months before symptoms appear

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
European Medical Journal

AI Model Predicts Cardiac Tamponade During Ablation

A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve ...
The American Journal of Managed Care

Intraocular Pressure in Glaucoma Predicted Through Machine-Learning Model

Machine learning accurately predicts peak and average IOP, aiding glaucoma management by informing treatment decisions. Random forest regression (RFR) outperformed ...
pv magazine International

AI now can now predict solar cell efficiency from wafer quality

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...
Medindia

Machine Learning Tool Predicts Post-Transplant Complications

BIOPREVENT’ AI tool predicts transplant-related immune conflict and mortality risk using biomarkers, helping doctors ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
The American Journal of Managed Care

Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes

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, ...