Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
Abstract 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, ho...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | Systematic Reviews |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13643-019-1074-9 |
| _version_ | 1818928044307382272 |
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| author | Iain J. Marshall Byron C. Wallace |
| author_facet | Iain J. Marshall Byron C. Wallace |
| author_sort | Iain J. Marshall |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-20T03:22:39Z |
| format | Article |
| id | doaj.art-a89a7991ef70442b97aa20a88d5ad6f7 |
| institution | Directory Open Access Journal |
| issn | 2046-4053 |
| language | English |
| last_indexed | 2024-12-20T03:22:39Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Systematic Reviews |
| spelling | doaj.art-a89a7991ef70442b97aa20a88d5ad6f72022-12-21T19:55:11ZengBMCSystematic Reviews2046-40532019-07-018111010.1186/s13643-019-1074-9Toward systematic review automation: a practical guide to using machine learning tools in research synthesisIain J. Marshall0Byron C. Wallace1School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King’s College LondonKhoury College of Computer Sciences, Northeastern UniversityAbstract 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.http://link.springer.com/article/10.1186/s13643-019-1074-9Machine learningNatural language processingEvidence synthesis |
| spellingShingle | Iain J. Marshall Byron C. Wallace Toward systematic review automation: a practical guide to using machine learning tools in research synthesis Systematic Reviews Machine learning Natural language processing Evidence 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 | Machine learning Natural language processing Evidence synthesis |
| url | http://link.springer.com/article/10.1186/s13643-019-1074-9 |
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