ascopubs.org

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams

We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University of ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

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 ...
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?
Open Heart

Development and validation of a 10-year predictive model for cardiovascular and metabolic disease risk: insights from a large-scale health examination cohort

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

Combining machine learning with statistical methods can provide accurate models for disease risk prediction

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...
Business Wire

Herophilus Publishes General Method for Detecting Relevant Signals in Machine Learning Analysis of Complex Biological Datasets

SAN FRANCISCO--(BUSINESS WIRE)--Herophilus, a leading biotechnology company developing neurotherapeutics to cure complex brain diseases, today announced the publication of research that describes a ...