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 ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

Scientists have created an AI model that forecasts moderate heat stress—a major precursor to coral bleaching—at sites along ...

Predictive Model of Acute Rectal Toxicity in Prostate Cancer Treated With Radiotherapy This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, ...

Using a real-world, nationwide electronic health record–derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a ...

The greatest risk in financial AI isn't that machines will make mistakes. It's that institutions will believe they understand those machines when they don't.