Semi-Supervised Learning to Improve Generalizability of Cancer Associated-Venous Thromboembolism Risk Prediction Models.

ABSTRACT: ObjectiveThe purpose of this study is to develop and validate an improved CA-VTE risk prediction model based on semi-supervised learning (SSL) algorithm.MethodsThis study used a combined retrospective and prospective cohort design. First, data from 2100 cancer patients in a tertiary hospital in Beijing were retrospectively collected, including a "labeled cohort" with CA-VTE outcomes (N = 1036) and an "unlabeled cohort" without outcomes (N = 1064). Then, another dataset were prospectively collected as an external validation set (N = 321). Eight supervised machine learning (ML) algorithms were used to develop CA-VTE risk prediction models and one SSL algorithm was used to improve generalizability of the models (pre- and post-imputation ML models). Model performance were

SUBMITTER: Jin S 

PROVIDER: S-EPMC12847654 | biostudies-literature | 2026 Jan-Dec

REPOSITORIES: biostudies-literature