Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

Cost-Effectiveness of Maintaining Higher Stem-Cell Collection Thresholds in the Chimeric Antigen Receptor T-Cell Era for Multiple Myeloma Predicting severe adverse events (SAEs) in oncology is ...

Mount Sinai researchers have created an analytic tool using machine learning that can predict cardiovascular disease risk in patients with obstructive sleep apnea ...

Kumo has unveiled KumoRFM-2, a next-generation foundation model designed specifically for structured enterprise data—marking ...

Combining machine learning and feature selection, this research accurately predicts aluminum levels in marine environments, ...

Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center ...

Innovative machine learning models using routine clinical data offer superior stroke risk prediction in atrial fibrillation, ...

A new study using US health survey data has developed a machine learning model that predicts osteoarthritis risk from exposure to volatile organic compounds (VOCs). The Linear Discriminant Analysis ...

In my latest Signal Spot, I had my Villanova students explore machine learning techniques to see if we could accurately ...