Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning.

ABSTRACT:

Background

This study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.

Methods

The clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiv

SUBMITTER: Hong L 

PROVIDER: S-EPMC9448978 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature