A new machine learning model built using a simple and interpretable approach predicts in-hospital death in patients with acute liver failure and reveals top risk drivers.

A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...

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 ...

Researchers developed and validated ElasticNet machine learning models that predict 12-month MMSE and BADL outcomes in ...

Feasibility and Implementation of a Digital Health Intervention Electronic Patient-Reported Outcomes–Based Platform for Telemonitoring Patients With Breast Cancer Undergoing Chemotherapy Among the 76 ...

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 ...

Understanding and preventing drug side effects holds a profound influence on drug development and utilization, profoundly impacting patients’ physical and mental well-being. Traditional artificial ...

Performance evaluation of an AI-powered system for clinical trial eligibility using mCODE data standards. ATheNa-Breast: A real-world pilot of an artificial intelligence (AI) chatbot using therapy ...

A new review highlights how machine learning is transforming the way scientists detect and measure organic pollutants in the ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...