A novel interpretative tool for early prediction of low cardiac output syndrome after valve surgery: online machine learning models.

ABSTRACT:

Objective

Low cardiac output syndrome (LCOS) is a severe complication after valve surgery, with no uniform standard for early identification. We developed interpretative machine learning (ML) models for predicting LCOS risk preoperatively and 0.5 h postoperatively for intervention in advance.

Methods

A total of 2218 patients undergoing valve surgery from June 2019 to Dec 2021 were finally enrolled to construct preoperative and postoperative models. Logistic regression, support vector machine (SVM), random forest classifier, extreme gradient boosting, and deep neural network were executed for model construction, and the performance of models was evaluated by area under the curve (AUC) of the receiver operating characteristic and calibration curves. Our models were interpreted

SUBMITTER: Hong L 

PROVIDER: S-EPMC10763875 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature