Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error.

Journal: Systematic reviews
Published Date: Jan 15, 2019

Abstract

BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review.

Authors

  • Alexandra Bannach-Brown
    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland. a.bannach-brown@ed.ac.uk.
  • Piotr Przybyła
    National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.
  • James Thomas
    EPPI-Centre, Social Research Institute, University College London, London, England, UK.
  • Andrew S C Rice
    Pain Research, Department of Surgery and Cancer, Imperial College, London, England.
  • Sophia Ananiadou
  • Jing Liao
    State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Malcolm Robert Macleod
    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland.

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