Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Inspired by how the human brain consolidates memory, the 'Nested Learning' framework allows different parts of a model to learn and update at different speeds.

This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine ...

Understanding how ozone behaves indoors is vital for assessing human health risks, as people spend most of their time inside.

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

As global participation in digital-asset ecosystems expands and blockchain behaviour becomes increasingly complex, platforms ...

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...