An interpretable machine learning model built from routine blood tests predicted major adverse cardiac and cerebrovascular ...

When machine learning models deliver problematic results, it can often happen in ways that humans can't make sense of, and this becomes dangerous when there are no limitations of the model, ...

Phytochemicals are bioactive compounds derived from plants, such as flavonoids, alkaloids and ginsenosides. They are key ...

Researchers propose a Holistic Explainable AI framework that embeds explanation into every stage of the data-analysis ...

Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health ...

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 ...

Engineers can 3D-print high-performance titanium parts for aircraft, medical implants and advanced machinery, but finding the ...

Creating machine learning models that generate accurate results is one thing, but it's quite another to ensure model interpretability -- the ability to understand why the ML models that power AI tools ...

Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights—including a model-estimated probability score for ...

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, ...