An open source machine learning framework for efficient and transparent systematic reviews
Schoot, Rens van de; Bruin, Jonathan de; Schram, Raoul; Zahedi, Parisa; Boer, Jan de; Weijdema, Felix; Kramer, Bianca; Huijts, Martijn; Hoogerwerf, Maarten; Ferdinands, Gerbrich; Harkema, Albert; Willemsen, Joukje; Ma, Yongchao; Fang, Qixiang; Hindriks, Sybren; Tummers, Lars; Oberski, Daniel
(2021) Nature Machine Intelligence, volume 3, issue 2, pp. 125 - 133
(Article)
Abstract
To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently
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screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.
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Keywords: Active learning, Machine learning, Open science, Researcher-in-the-loop, Systematic reviewing, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Human-Computer Interaction, Software
ISSN: 2522-5839
Publisher: Springer Nature
Note: Funding Information: We would like to thank the Utrecht University Library, focus area Applied Data Science, and departments of Information and Technology Services, Test and Quality Services, and Methodology and Statistics, for their support. We also want to thank all researchers who shared data, participated in our user experience tests or who gave us feedback on ASReview in other ways. Furthermore, we would like to thank the editors and reviewers for providing constructive feedback. This project was funded by the Innovation Fund for IT in Research Projects, Utrecht University, the Netherlands. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
(Peer reviewed)
