Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.

ABSTRACT:

Background

Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation.

Method

A systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet.

Res

SUBMITTER: Gholamzadeh M 

PROVIDER: S-EPMC9784000 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature