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

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

Two complementary predictors (DAAE-M and ELIE) estimate individualized 5-year progression risk using routine clinical data, ...

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

Using Python, web scraping, and advanced algorithms, the solution aggregates real-time data from marketplaces to deliver ...

This study highlights the potential for using deep learning methods on longitudinal health data from both primary and ...

In recent years, artificial intelligence has become more accessible than ever before. Powerful libraries, automated platforms ...

A machine learning model analyzing CpG-based DNA methylation accurately predicted the origin of many different cancer types ...

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 ...

A new review in Science China Life Sciences examines how machine learning and host-microbiome multi-omics can be combined to better understand health and disease. The article outlines the road from ...

A biology-guided artificial intelligence model applied to routine pathology slides accurately predicted outcomes and response to ...

A biology-guided artificial intelligence model applied to routine pathology slides accurately predicted outcomes and response to immunotherapy in patients with metastatic non-small cell lung ...