Machine learning computational tools to assist the performance of systematic reviews: A mapping review.

ABSTRACT:

Background

Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Due its methodology, SR require significant time and resources to be performed; they also require repetitive steps that may introduce biases and human errors. Machine learning (ML) algorithms therefore present a promising alternative and a potential game changer to speed up and automate the SR process. This review aims to map the current availability of computational tools that use ML techniques to assist in the performance of SR, and to support authors in the selection of the right software for the performance of evidence synthesis.

Methods

The mapping review

SUBMITTER: Cierco Jimenez R 

PROVIDER: S-EPMC9756658 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature