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Publication - Dr Louise Millard

    Machine learning to assist risk of bias assessments in systematic reviews


    Millard, LAC, Flach, PA & Higgins, JPT, 2016, ‘Machine learning to assist risk of bias assessments in systematic reviews’. International Journal of Epidemiology, vol 45., pp. 266-277


    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 concealment and blinding.
    The first model predicts whether a sentence in a research article
    relevant information. The second model predicts a
    risk-of-bias value for each research article. We use logistic
    where each independent variable is the frequency of
    a word in a sentence or article, respectively.

    Results: We found that
    sentences can be successfully ranked by relevance with area under the
    receiver operating characteristic (ROC)
    curve (AUC) > 0.98. Articles can be ranked by
    risk of bias with AUC > 0.72. We estimate that more than 33% of
    articles can
    be assessed by just one reviewer, where two
    reviewers are normally required.

    Conclusions: We show that text mining can be used to assist risk-of-bias assessments.

    Full details in the University publications repository