Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow.

ABSTRACT:

Background

Current text mining tools supporting abstract screening in systematic reviews are not widely used, in part because they lack sensitivity and precision. We set out to develop an accessible, semi-automated "workflow" to conduct abstract screening for systematic reviews and other knowledge synthesis methods.

Methods

We adopt widely recommended text-mining and machine-learning methods to (1) process title-abstracts into numerical training data; and (2) train a classification model to predict eligible abstracts. The predicted abstracts are screened by human reviewers for ("true") eligibility, and the newly eligible abstracts are used to identify similar abstracts, using near-neighbor methods, which are also screened. These abstracts, as well as their eligibility result

SUBMITTER: Pham B 

PROVIDER: S-EPMC8152711 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature