Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies.

ABSTRACT: Objective: This study aimed to systematically assess the characteristics and risk of bias of previous studies that have investigated nonlinear machine learning algorithms for warfarin dose prediction. Methods: We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), and Wanfang Database up to March 2022. We assessed the general characteristics of the included studies with respect to the participants, predictors, model development, and model evaluation. The methodological quality of the studies was determined, and the risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: From a total of

SUBMITTER: Zhang F 

PROVIDER: S-EPMC9147332 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature