Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study.

ABSTRACT:

Background

Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and machine learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.

Methods

In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to

SUBMITTER: Zimmerman J 

PROVIDER: S-EPMC8017891 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature