Computer aided systematic review: research and application_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Xueqin ¹ , ZHANG Wei ¹ , ZHENG Peiyong ² ,  DENG Hongyong ¹

1. TCM Health Service Collaborative Innovation Center, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P.R.China;
2. Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, P.R.China;

Corresponding author：

DENG Hongyong, Email: denghy@shutcm.edu.cn

Keywords：

Systematic review; Computer technology; Evidence-based medicine; Methodology

DOI：

10.7507/1672-2531.202006046

Video：

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Systematic review is an important method to obtain clinical decision evidence. The traditional systematic review is primarily conducted manually, which cannot meet the needs of rapid decision-making due to its high time and labor force cost as well as low efficiency. However, the development of information technology has laid the foundation for computer-aided systematic review methods. Attempts have been made to replace or enhance manual operations by introducing computer technology in all aspects of systematic review, thereby improving efficiency. This paper integrates the methodological research and its application of computer-aided systematic review both domestically and abroad from perspectives of literature acquisition, data processing and evidence evaluation. The aim of this paper is to understand the status quo and future trend in this field, and to provide reference for further researches related to automated systematic review technology.

Citation： ZHANG Xueqin, ZHANG Wei, ZHENG Peiyong, DENG Hongyong. Computer aided systematic review: research and application. Chinese Journal of Evidence-Based Medicine, 2021, 21(1): 111-116. doi: 10.7507/1672-2531.202006046 Copy

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1. Thomas J, Noel-Storr A, Marshall I, et al. Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol, 2017, 91: 31-37.
2. Elliott JH, Synnot A, Turner T, et al. Living systematic review: 1. Introduction-the why, what, when, and how. J Clin Epidemiol, 2017, 91: 23-30.
3. Bertram L, McQueen MB, Mullin K, et al. Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet, 2007, 39(1): 17-23.
4. Maraver P, Armañanzas R, Gillette TA, et al. PaperBot: open-source web-based search and metadata organization of scientific literature. BMC Bioinform, 2019, 20(1): 50.
5. Sunwon L, Donghyeon K, Kyubum L, et al. BEST: next-generation biomedical entity search tool for knowledge discovery from biomedical literature. PLoS One, 2016, 11(10): e0164680.
6. Wei Y, Melinda C, Siobhan MD, et al. GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinform, 2008, 9(1): 205-214.
7. Wallace BC, Trikalinos TA, Lau J, et al. Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinform, 2010, 11(1): 55-66.
8. Wallace BC, Noel-Storr A, Marshall IJ, et al. Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach. J Am Med Inform Assoc, 2017, 24(6): 1165-1168.
9. Brassey J, Price C, Edwards J, et al. Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence. BMJ Evid Based Med, 2019, DOI: 10.1136/bmjebm-2018-111126.
10. 倪亚晖, 王玮健, 赵锦春, 等. 一种基于模式识别技术在标准化文献元数据提取实现方法. 中国专利: 110110097A, 2019-05-13.
11. 王朝阳, 翁鸿, 靳英辉, 等. 如何采用RevMan 5. 3软件实现单核苷酸多态性数据的Meta分析. 中国循证医学杂志, 2018, 18(2): 244-248.
12. 邓宏勇. PythonMeta1.11. 2019-7-23. Available at: https://pypi.org/project/PythonMeta/.
13. 邓宏勇. PyMeta. 2018-2020. Available at: https://www.pymeta.com/.
14. 张薇, 许吉, 邓宏勇. 国际医学证据分级与推荐体系发展及现状. 中国循证医学杂志, 2019, 19(11): 1373-1378.
15. Marshall IJ, Kuiper J, Wallace BC. RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. J Am Med Inform Assoc, 2016, 23(1): 193-201.
16. 陈昊, 曾宪涛, 谷万杰, 等. 更新版Guideline Development Tool (GRADE pro GDT)在干预性临床实践指南制定中的应用. 中国循证医学杂志, 2018, 18(10): 1135-1142.
17. Papakonstantinou T, Nikolakopoulou A, Higgins JP, et al. CINeMA: Software for semiautomated assessment of the confidence in the results of network meta‐analysis. System Rev, 2020, 16(1).
18. Adriani N, Julian PT, Theodoros P. CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Med, 2020, 17(4): e1003082.
19. Kitchenham B, Charters S. Guidelines for performing systematic literature reviews in software engineering (Version 2.3). Available at: https://parsif.al/.
20. Fabbri S, Octaviano F, Silva C, et al. Improvements in the StArt tool to better support the systematic review process, 2016. Available at:http://lapes.dc.ufscar.br/tools/start.
21. Christopher M. Systematic review toolbox, 2015. Available at: http://systematicrevie wtools.com/.
22. 第十届亚太地区循证医学研讨会/第三届循证中医药学国际论坛/首届世界人工智能健康管理论坛在天津召开. 中国循证医学杂志, 2018, 18(10): 1069.
23. Annette MO, Guy T, Stephen BG, et al. Still moving toward automation of the systematic review process: a summary of discussions at the third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR). System Rev, 2019, 8(1): 57.

Chinese Journal of Evidence-Based Medicine

Computer aided systematic review: research and application

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