Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study.

Journal: Systematic reviews
Published Date: Nov 15, 2019

Abstract

BACKGROUND: Web applications that employ natural language processing technologies to support systematic reviewers during abstract screening have become more common. The goal of our project was to conduct a case study to explore a screening approach that temporarily replaces a human screener with a semi-automated screening tool.

Authors

  • Gerald Gartlehner
    RTI International-University of North Carolina Evidence-based Practice Center, Research Triangle Park, NC, USA. ggartlehner@rti.org.
  • Gernot Wagner
    Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria.
  • Linda Lux
    RTI International-University of North Carolina Evidence-based Practice Center, Research Triangle Park, NC, USA.
  • Lisa Affengruber
    Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria.
  • Andreea Dobrescu
    Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria.
  • Angela Kaminski-Hartenthaler
    Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria.
  • Meera Viswanathan
    RTI International-University of North Carolina Evidence-based Practice Center, Research Triangle Park, NC, USA.

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