Machine learning-based prediction of health outcomes in pediatric organ transplantation recipients.

ABSTRACT:

Objectives

Prediction of post-transplant health outcomes and identification of key factors remain important issues for pediatric transplant teams and researchers. Outcomes research has generally relied on general linear modeling or similar techniques offering limited predictive validity. Thus far, data-driven modeling and machine learning (ML) approaches have had limited application and success in pediatric transplant outcomes research. The purpose of the current study was to examine ML models predicting post-transplant hospitalization in a sample of pediatric kidney, liver, and heart transplant recipients from a large solid organ transplant program.

Materials and methods

Various logistic regression, naive Bayes, support vector machine, and deep learning (DL) methods were us

SUBMITTER: Killian MO 

PROVIDER: S-EPMC7952224 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature