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, Arma?anzas 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. 倪亞暉, 王瑋健, 趙錦春, 等. 一種基于模式識別技術在標準化文獻元數據提取實現方法. 中國專利: 110110097A, 2019-05-13.
11. 王朝陽, 翁鴻, 靳英輝, 等. 如何采用RevMan 5. 3軟件實現單核苷酸多態性數據的Meta分析. 中國循證醫學雜志, 2018, 18(2): 244-248.
12. 鄧宏勇. PythonMeta1.11. 2019-7-23. Available at: https://pypi.org/project/PythonMeta/.
13. 鄧宏勇. PyMeta. 2018-2020. Available at: https://www.pymeta.com/.
14. 張薇, 許吉, 鄧宏勇. 國際醫學證據分級與推薦體系發展及現狀. 中國循證醫學雜志, 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. 陳昊, 曾憲濤, 谷萬杰, 等. 更新版Guideline Development Tool (GRADE pro GDT)在干預性臨床實踐指南制定中的應用. 中國循證醫學雜志, 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. 第十屆亞太地區循證醫學研討會/第三屆循證中醫藥學國際論壇/首屆世界人工智能健康管理論壇在天津召開. 中國循證醫學雜志, 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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