A Bayesian approach to predictive uncertainty in chemotherapy patients at risk of acute care utilization.

ABSTRACT:

Background

Machine learning (ML) predictions are becoming increasingly integrated into medical practice. One commonly used method, ℓ₁-penalised logistic regression (LASSO), can estimate patient risk for disease outcomes but is limited by only providing point estimates. Instead, Bayesian logistic LASSO regression (BLLR) models provide distributions for risk predictions, giving clinicians a better understanding of predictive uncertainty, but they are not commonly implemented.

Methods

This study evaluates the predictive performance of different BLLRs compared to standard logistic LASSO regression, using real-world, high-dimensional, structured electronic health record (EHR) data from cancer patients initiating chemotherapy at a comprehensive cancer centre. Multiple B

SUBMITTER: Fanconi C 

PROVIDER: S-EPMC10250586 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature