Machine Learning for Mortality Prediction in Pediatric Myocarditis.

ABSTRACT: Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5-8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported. Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.

SUBMITTER: Chou FS 

PROVIDER: S-EPMC8102689 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature