Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery-a retrospective cohort study.

ABSTRACT:

Background

Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicting four major APOs after pediatric congenital heart surgery and their clinically meaningful model interpretations.

Methods

Between August 2014 and December 2021, 23,000 consecutive pediatric patients receiving congenital heart surgery were enrolled. Based on the split date of 1 January 2019, we selected 13,927 participants for the training cohort, and 9,073 participants for the testing cohort. Four predefined major APOs including low cardiac output syndrome (LCOS), pneumonia, renal failure, and deep venous thrombosis (DVT) were in

SUBMITTER: Tong C 

PROVIDER: S-EPMC11020051 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature