Machine learning model predicts clotting risk during CRRT in ESKD patients: a SHAP-interpretable approach.

ABSTRACT: Ensuring fluent extracorporeal circulation and preventing circuit clotting are important for end-stage kidney disease (ESKD) patients undergoing continuous renal replacement therapy (CRRT). This study aimed to develop a predictive model using machine learning (ML) algorithms to evaluate clotting risk after initiating CRRT, enhancing treatment safety and effectiveness. This study involved 636 ESKD patients who underwent CRRT. Feature selection was conducted via the least absolute shrinkage and selection operator (LASSO) algorithm. ML algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machine (GBM), decision tree, and logistic regression (LR), were applied to construct models through tenfold cross-validation.

SUBMITTER: Qiu S 

PROVIDER: S-EPMC12517423 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature