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Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these t...

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Detalles Bibliográficos
Autores principales: Marshall, Iain J., Wallace, Byron C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6621996/
https://www.ncbi.nlm.nih.gov/pubmed/31296265
http://dx.doi.org/10.1186/s13643-019-1074-9
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author Marshall, Iain J.
Wallace, Byron C.
author_facet Marshall, Iain J.
Wallace, Byron C.
author_sort Marshall, Iain J.
collection PubMed
description Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.
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spelling pubmed-66219962019-07-22 Toward systematic review automation: a practical guide to using machine learning tools in research synthesis Marshall, Iain J. Wallace, Byron C. Syst Rev Commentary Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice. BioMed Central 2019-07-11 /pmc/articles/PMC6621996/ /pubmed/31296265 http://dx.doi.org/10.1186/s13643-019-1074-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Commentary
Marshall, Iain J.
Wallace, Byron C.
Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
title Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
title_full Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
title_fullStr Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
title_full_unstemmed Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
title_short Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
title_sort toward systematic review automation: a practical guide to using machine learning tools in research synthesis
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6621996/
https://www.ncbi.nlm.nih.gov/pubmed/31296265
http://dx.doi.org/10.1186/s13643-019-1074-9
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