A new framework could transform national flood prediction

Office of Water Prediction (OWP) makes critical flood forecasts with the National Water Model. Despite improvements over time ...

AI method accelerates liquid simulations by learning fundamental physical relationships

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...
AHA/ASA Journals

Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart ...

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...
Rochester Institute of Technology

Researchers develop new method for predicting chaos

RIT researchers publish a paper in Nature Scientific Reports on a new tree-based machine learning algorithm used to predict chaos.
Open Heart

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation

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 ...
IEEE

Machine Learning-Based Traffic Flow Prediction and Management

Abstract: Artificial Intelligence (AI) and data analytics are used by a machine learning-based traffic prediction and management to optimize transport networks, improve safety, and improve traffic ...
Medical Xpress

AI model identifies how every country can improve its cancer outcomes

For the first time, researchers have used machine learning—a type of artificial intelligence (AI)—to identify the most important drivers of cancer survival in nearly all the countries in the world.
Frontiers

Early prediction of ARDS caused by non-pulmonary sepsis based on machine learning algorithms of inflammatory indicators and blood gas parameters

Objective: Acute respiratory distress syndrome (ARDS) is a common complication in patients with non-pulmonary sepsis. Early identification and prediction of the occurrence of ARDS in non-pulmonary ...
The New York Times

Best College Football Playoff matchups, worst draw, early picks and more first thoughts

Texas Tech has a first-round bye and will play the winner of Oregon-JMU in the Orange Bowl. Stacy Revere / Getty Images After months of predictions and speculation, the College Football Playoff ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Individual prediction uncertainty is a key aspect of clinical prediction model performance; however ...
Frontiers

An interpretable machine learning model for early prediction of Escherichia coli infection in ICU patients

Background: Early and accurate identification of Escherichia coli (E. coli) infection in intensive care unit (ICU) patients remains challenging butmay improve clinical outcomes if addressed ...