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 ...
The American Journal of Managed Care

Machine Learning May Enable Earlier Detection of CKD Risk Factors

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
TWCN Tech News

Best Machine Learning Models to know

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 ...
ascopubs.org

Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence

NOTE. These are the baseline variables determined at treatment completion and included in the analysis. Abbreviations: CIN, cervical intraepithelial neoplasia; COPD, chronic obstructive pulmonary ...
BMJ

Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...
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 ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...