The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
AHA/ASA Journals

Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart ...

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...
The Motley Fool

What Is a Statistical Model? Overview and Example

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...
EurekAlert!

Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model

The Niño3.4 SST anomalies during 2020–2024 for reanalyses from GODAS (black dotted line) and predictions made by statistical (green dotted lines) and dynamic (blue dotted lines) models from the IRI ...
Engadget

DeepMind releases a new weather forecasting model for more accurate predictions

Google's DeepMind just released WeatherNext 2, a new version of its AI weather prediction model. The company promises that it "delivers more efficient, more accurate and higher-resolution global ...