Machine learning to assist risk-of-bias assessments in systematic reviews.

ABSTRACT:

Background

Risk-of-bias assessments are now a standard component of systematic reviews. At present, reviewers need to manually identify relevant parts of research articles for a set of methodological elements that affect the risk of bias, in order to make a risk-of-bias judgement for each of these elements. We investigate the use of text mining methods to automate risk-of-bias assessments in systematic reviews. We aim to identify relevant sentences within the text of included articles, to rank articles by risk of bias and to reduce the number of risk-of-bias assessments that the reviewers need to perform by hand.

Methods

We use supervised machine learning to train two types of models, for each of the three risk-of-bias properties of sequence generation, allocation concealmen

SUBMITTER: Millard LA 

PROVIDER: S-EPMC4795562 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature