Literature DB >> 34550582

Semi-automated Tools for Systematic Searches.

Gaelen P Adam1, Byron C Wallace2, Thomas A Trikalinos3.   

Abstract

Traditionally, literature identification for systematic reviews has relied on a two-step process: first, searching databases to identify potentially relevant citations, and then manually screening those citations. A number of tools have been developed to streamline and semi-automate this process, including tools to generate terms; to visualize and evaluate search queries; to trace citation linkages; to deduplicate, limit, or translate searches across databases; and to prioritize relevant abstracts for screening. Research is ongoing into tools that can unify searching and screening into a single step, and several protype tools have been developed. As this field grows, it is becoming increasingly important to develop and codify methods for evaluating the extent to which these tools fulfill their purpose.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Information science; Literature identification; Machine learning; Systematic review methods; Text mining

Mesh:

Year:  2022        PMID: 34550582     DOI: 10.1007/978-1-0716-1566-9_2

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  28 in total

1.  Estimating time to conduct a meta-analysis from number of citations retrieved.

Authors:  I E Allen; I Olkin
Journal:  JAMA       Date:  1999-08-18       Impact factor: 56.272

2.  The Use of Rapid Review Methods for the U.S. Preventive Services Task Force.

Authors:  Carrie D Patnode; Michelle L Eder; Emily S Walsh; Meera Viswanathan; Jennifer S Lin
Journal:  Am J Prev Med       Date:  2018-01       Impact factor: 5.043

3.  A Text-Mining Framework for Supporting Systematic Reviews.

Authors:  Dingcheng Li; Zhen Wang; Liwei Wang; Sunghwan Sohn; Feichen Shen; Mohammad Hassan Murad; Hongfang Liu
Journal:  Am J Inf Manag       Date:  2016-08-31

4.  Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews.

Authors:  Ian Shemilt; Antonia Simon; Gareth J Hollands; Theresa M Marteau; David Ogilvie; Alison O'Mara-Eves; Michael P Kelly; James Thomas
Journal:  Res Synth Methods       Date:  2013-08-23       Impact factor: 5.273

Review 5.  Using text mining for study identification in systematic reviews: a systematic review of current approaches.

Authors:  Alison O'Mara-Eves; James Thomas; John McNaught; Makoto Miwa; Sophia Ananiadou
Journal:  Syst Rev       Date:  2015-01-14

Review 6.  A scoping review of rapid review methods.

Authors:  Andrea C Tricco; Jesmin Antony; Wasifa Zarin; Lisa Strifler; Marco Ghassemi; John Ivory; Laure Perrier; Brian Hutton; David Moher; Sharon E Straus
Journal:  BMC Med       Date:  2015-09-16       Impact factor: 8.775

Review 7.  What is an evidence map? A systematic review of published evidence maps and their definitions, methods, and products.

Authors:  Isomi M Miake-Lye; Susanne Hempel; Roberta Shanman; Paul G Shekelle
Journal:  Syst Rev       Date:  2016-02-10

Review 8.  A scoping review of scoping reviews: advancing the approach and enhancing the consistency.

Authors:  Mai T Pham; Andrijana Rajić; Judy D Greig; Jan M Sargeant; Andrew Papadopoulos; Scott A McEwen
Journal:  Res Synth Methods       Date:  2014-07-24       Impact factor: 5.273

9.  Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide.

Authors:  Iain J Marshall; Anna Noel-Storr; Joël Kuiper; James Thomas; Byron C Wallace
Journal:  Res Synth Methods       Date:  2018-02-07       Impact factor: 5.273

10.  Semi-automated screening of biomedical citations for systematic reviews.

Authors:  Byron C Wallace; Thomas A Trikalinos; Joseph Lau; Carla Brodley; Christopher H Schmid
Journal:  BMC Bioinformatics       Date:  2010-01-26       Impact factor: 3.169

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