Application of nature language processing in systematic reviews_Chinese Journal of Evidence-Based Medicine

Authors：

QIN Xuan , LIU Jiali , WANG Yuning , DENG Ke , MA Yu , ZOU Kang ,  LI Ling ,  SUN Xin

Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

LI Ling, Email: liling@wchscu.cn; SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Nature language processing; Machine learning; Systematic review; Literature screening; Data extraction

DOI：

10.7507/1672-2531.202012150

Video：

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Systematic reviews can provide important evidence support for clinical practice and health decision-making. In this process, literature screening and data extraction are extensively time-consuming procedures. Natural language processing (NLP), as one of the research directions of computer science and artificial intelligence, can accelerate the process of literature screening and data extraction in systematic reviews. This paper introduced the requirements of systematic reviews for rapid literature screening and data extraction, the development of NLP and types of machine learning; and systematically collated the NLP tools for the title and abstract screening, full-text screening and data extraction in systematic reviews; and discussed the problems in the application of NLP tools in the field of systematic reviews and proposed a prospect for its future development.

Citation： QIN Xuan, LIU Jiali, WANG Yuning, DENG Ke, MA Yu, ZOU Kang, LI Ling, SUN Xin. Application of nature language processing in systematic reviews. Chinese Journal of Evidence-Based Medicine, 2021, 21(6): 715-720. doi: 10.7507/1672-2531.202012150 Copy

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11.	Touloupou P, Finkenstädt B, Spencer SEF. Scalable bayesian inference for coupled hidden markov and semi-markov models. J Comput Graph Stat, 2019, 29(2): 238-249.
12.	Zu B, Xia K, Pan Y, et al. A novel graph constructor for semisupervised discriminant analysis: combined low-rank and k-nearest neighbor graph. Comput Intell Neurosci, 2017, 2017: 9290230.
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14.	Shen Y, Li Y, Zheng HT, et al. Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier. BMC Bioinformatics, 2019, 20(1): 330.
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35.	Yu W, Clyne M, Dolan SM, et al. GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinformatics, 2008, 9: 205.
36.	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.
37.	Cao X, Maloney KB, Brusic V. Data mining of cancer vaccine trials: a bird's-eye view. Immunome Res, 2008, 4: 7.
38.	Jap J, Saldanha IJ, Smith BT, et al. Features and functioning of data abstraction assistant, a software application for data abstraction during systematic reviews. Res Synth Methods, 2019, 10(1): 2-14.
39.	Kuiper J, Marshall IJ, Wallace BC, et al. Spá: A web-based viewer for text mining in evidence based medicine. Nancy: European Conference, ECML PKDD, 2014: 452-455.
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1. Sutherland SE. An introduction to systematic reviews. J Evid Based Dent Pract, 2004, 4(1): 47-51.
2. Speaker SL. An historical overview of the national network of libraries of medicine, 1985-2015. J Med Libr Assoc, 2018, 106(2): 162-174.
3. McKibbon KA, Wilczynski NL, Haynes RB, et al. Retrieving randomized controlled trials from medline: a comparison of 38 published search filters. Health Info Libr J, 2009, 26(3): 187-202.
4. Tarsilla M. Cochrane handbook for systematic reviews of interventions. J Mul Eval, 2008, 6: 142-148.
5. Sampson M, Shojania KG, Garritty C, et al. Systematic reviews can be produced and published faster. J Clin Epidemiol, 2008, 61(6): 531-536.
6. Shojania KG, Sampson M, Ansari MT, et al. How quickly do systematic reviews go out of date? A survival analysis. Ann Intern Med, 2007, 147(4): 224-233.
7. Higgins, JPT. Cochrane handbook for systematic reviews of interventions version 5.0. 2. The Cochrane Collaboration, 2011. Available at: http://www.cochrane-handbook.org.
8. Borah R, Brown AW, Capers PL, et al. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open, 2017, 7(2): e012545.
9. Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med, 2010, 7(9): e1000326.
10. 王煜, 邓晖, 李晓瑶, 等. 自然语言处理技术在建筑工程中的应用研究综述. 图学学报, 2020, 41(4): 501-511.
11. Touloupou P, Finkenstädt B, Spencer SEF. Scalable bayesian inference for coupled hidden markov and semi-markov models. J Comput Graph Stat, 2019, 29(2): 238-249.
12. Zu B, Xia K, Pan Y, et al. A novel graph constructor for semisupervised discriminant analysis: combined low-rank and k-nearest neighbor graph. Comput Intell Neurosci, 2017, 2017: 9290230.
13. Saunders C, Stitson MO, Weston J, et al. Support vector machine. Comput Sci, 2002, 1(4): 1-28.
14. Shen Y, Li Y, Zheng HT, et al. Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier. BMC Bioinformatics, 2019, 20(1): 330.
15. Alfattni G, Peek N, Nenadic G. Extraction of temporal relations from clinical free text: A systematic review of current approaches. J Biomed Inform, 2020, 108: 103488.
16. Bojanowski P, Grave E, Joulin A, et al. Enriching word vectors with subword information. Trans Assoc Comput Linguist, 2017, 5: 135-146.
17. Collobert R, Weston J. A unified architecture for natural language processing: deep neural networks with multitask learning. Helsinki: Proceedings of the 25th international conference on Machine learning, 2008.
18. Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: lstm cells and network architectures. Neural Comput, 2019, 31(7): 1235-1270.
19. Hu G, Zhang L, Shen P, et al. Multimodal gesturerecognition using 3-D convolution and convolutional LSTM. IEEE Access, 2017, 5: 4517-4524.
20. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need, 2017-06-01. Available at: https://arxiv.org/abs/1706.03762.
21. Devlin J, Chang MW, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding, 2018. Available at: https://www.aclweb.org/anthology/N19-1423/.
22. Russell S, Norvig P. Artificial intelligence: a modern approach: by stuart russell and Peter Norwig, Prentice Hall. Englewood Cliffs: Series in Artificial Intelligence, 1995.
23. Jennings NR, Wooldridge MJ. Foundations of machine learning. Boston: MIT Press, 2012.
24. Gates A, Johnson C, Hartling L. Technology-assisted title and abstract screening for systematic reviews: a retrospective evaluation of the Abstrackr machine learning tool. Syst Rev, 2018, 7(1): 45.
25. Wallace BC, Small K, Brodley CE, et al. Deploying an interactive machine learning system in an evidence-based practice center: abstrackr. Miami: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 2012.
26. Thomas J, Brunton J. EPPI-Reviewer 4: software for research synthesis, 2010. Available at: http://eppi.ioe.ac.uk/cms/.
27. Ouzzani M, Hammady H, Fedorowicz Z, et al. Rayyan-a web and mobile app for systematic reviews. Syst Rev, 2016, 5(1): 210.
28. Schoot R, Bruin J, Schram R, et al. ASReview: open source software for efficient and transparent active learning for systematic reviews. Available at: https://arxiv.org/abs/2006.12166.
29. Varghese A, Cawley M, Hong T. Supervised clustering for automated document classification and prioritization: a case study using toxicological abstracts. Environ Syst Decis, 2018, 38(3): 398-414.
30. Cheng SH, Augustin C, Bethel A, et al. Using machine learning to advance synthesis and use of conservation and environmental evidence. Conserv Biol, 2018, 32(4): 762-764.
31. Howard BE, Phillips J, Tandon A, et al. SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation. Environ Int, 2020, 138: 105623.
32. Harrison H, Griffin SJ, Kuhn I, et al. Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol, 2020, 20(1): 7.
33. Van der Mierden S, Tsaioun K, Bleich A, et al. Software tools for literature screening in systematic reviews in biomedical research. ALTEX, 2019, 36(3): 508-517.
34. Tsou AY, Treadwell JR, Erinoff E, et al. Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer. Syst Rev, 2020, 9(1): 73.
35. Yu W, Clyne M, Dolan SM, et al. GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinformatics, 2008, 9: 205.
36. 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.
37. Cao X, Maloney KB, Brusic V. Data mining of cancer vaccine trials: a bird's-eye view. Immunome Res, 2008, 4: 7.
38. Jap J, Saldanha IJ, Smith BT, et al. Features and functioning of data abstraction assistant, a software application for data abstraction during systematic reviews. Res Synth Methods, 2019, 10(1): 2-14.
39. Kuiper J, Marshall IJ, Wallace BC, et al. Spá: A web-based viewer for text mining in evidence based medicine. Nancy: European Conference, ECML PKDD, 2014: 452-455.
40. Clark J, Glasziou P, Del Mar C, et al. A full systematic review was completed in 2 weeks using automation tools: a case study. J Clin Epidemiol, 2020, 121: 81-90.

Chinese Journal of Evidence-Based Medicine

Application of nature language processing in systematic reviews

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