Prediction of the short-term prognosis of acute ischaemic stroke in patients with high treatment platelet reactivity using explainable machine learning.

ABSTRACT: The aim of this study is to establish and validate an optimal explainable prediction model based on a machine learning (ML) approach to predict the short-term prognosis in high on-treatment platelet reactivity (HTPR) individuals with acute ischaemic stroke (AIS). Using individual basic characteristics, blood test indices, and the CYP2C19 genotype, a model to predict a poor functional prognosis (modified Rankin scale score ≥ 3) was constructed based on ML models, including logistic regression, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and light gradient boosting machine. On this basis, global and local interpretability techniques were used to interpret selected ML models and explore the risk factors affecting the short-term prognosis of AIS in pat

SUBMITTER: Liu J 

PROVIDER: S-EPMC12541029 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature