Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records Data collected in the multicentric PRAIS ...

In past roles, I’ve spent countless hours trying to understand why state-of-the-art models produced subpar outputs. The underlying issue here is that machine learning models don’t “think” like humans ...

A new study explores how artificial intelligence models can support clinical decision-making for sepsis management. Their research, titled “Responsible AI for Sepsis Prediction: Bridging the Gap ...

In a significant breakthrough, researchers have developed an advanced explainable deep learning model to predict and analyze harmful algal blooms (HABs) in freshwater lakes and reservoirs across China ...

What does gentrification in Philadelphia look like? “High-rise, modern apartment buildings.” “(A) modern look that’s so out of place with our traditional row homes that have been here for a hundred ...

This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest ...

With that in mind, “Regulatory agencies have to do more to provide practical guidance to the industry.” There are three technical enablers “that repeatedly separate successful [AI] deployments from ...

AI medical diagnosis apps offer major opportunities in enhancing diagnostic accuracy and efficiency through AI algorithms.

The Uncertainty Engine is guiding research in fusion plasma physics. Could similar approaches benefit fission research as ...