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

Journal: International journal of epidemiology
Published Date:

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.

Authors

  • Louise A C Millard
    MRC Integrative Epidemiology Unit, School of Social and Community Medicine and Intelligent Systems Laboratory, University of Bristol, Bristol, UK louise.millard@bristol.ac.uk.
  • Peter A Flach
    MRC Integrative Epidemiology Unit, Intelligent Systems Laboratory, University of Bristol, Bristol, UK.
  • Julian P T Higgins
    MRC Integrative Epidemiology Unit, School of Social and Community Medicine and.