Deep learning model enables accurate and scalable drug-drug interaction prediction

Managing complex medical conditions often requires the simultaneous use of multiple different drugs, referred to as polypharmacy. While necessary, this significantly increases the risk of drug-drug ...
Medical Xpress

A new tool to predict physical health risks in young people with psychosis

A new clinic-ready web-based risk prediction tool called PsyMetRiC is now available to forecast the risk of young people with psychosis developing cardiometabolic disorders such as obesity, metabolic ...
Tech Xplore

Improving AI models' ability to explain their predictions

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept ...
Open Heart

Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
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 identification to improve prevention and management strategies. Traditional risk ...
IEEE

Smart Diabetes Prediction System Using Machine Learning Algorithms

Abstract: Diabetes mellitus is a chronic condition that continues to rise globally, with Type 2 diabetes being the most prevalent form. Early detection and prediction of diabetes risk are crucial to ...
Frontiers

Early prediction of gestational diabetes mellitus using machine learning-integrated metabolomic and clinical features

Gestational diabetes mellitus (GDM), a prevalent metabolic disorder associated with pregnancy, which often postpones intervention until after metabolic complications have developed. This study seeks ...
ahajournals.org

Abstract 4340723: Comparative Study of Coronary Artery Disease (CAD) Prediction: Conventional QRISK3 vs. Enhanced Machine Learning Models Combined with Particle Swarm ...

Background: Coronary Artery Disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known ...
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, ...
ahajournals.org

Abstract 010: Machine Learning and Longitudinal Blood Pressure Data Enhance Prediction of Hypertensive Disorders of Pregnancy

Introduction: Early identification of pregnant women at risk of hypertensive disorders of pregnancy (HDP) is critical for improving pregnancy outcomes. The USPSTF risk stratification approach ...