Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study.

Journal: Systematic reviews
Published Date: Apr 2, 2021

Abstract

BACKGROUND: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and machine learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs.

Authors

  • John Zimmerman
    Deloitte Consulting LLP, Atlanta, GA, USA.
  • Robin E Soler
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation, 1600 Clifton Rd, Atlanta, GA, USA.
  • James Lavinder
    Deloitte Consulting, LLP, 191 Peachtree Street, Atlanta, GA, 30303, USA.
  • Sarah Murphy
    Deloitte Consulting, LLP, 191 Peachtree Street, Atlanta, GA, 30303, USA.
  • Charisma Atkins
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation, 1600 Clifton Rd, Atlanta, GA, USA.
  • LaShonda Hulbert
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation, 1600 Clifton Rd, Atlanta, GA, USA.
  • Richard Lusk
    Deloitte Consulting, LLP, 191 Peachtree Street, Atlanta, GA, 30303, USA.
  • Boon Peng Ng
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation, 1600 Clifton Rd, Atlanta, GA, USA.

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