Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans.

ABSTRACT: Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regress

SUBMITTER: Steiner HE 

PROVIDER: S-EPMC8585774 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature