Supporting systematic reviews using LDA-based document representations

Systematic Reviews

Volumn 4, Issue 1, 2015, Pages

  Mo, Yuanhan ^a   Kontonatsios, Georgios ^a   Ananiadou, Sophia ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Machine learning; Systematic reviews; Text mining; Topic model

Indexed keywords

ACCURACY; ARTICLE; AUTOMATED PATTERN RECOGNITION; HUMAN; MACHINE LEARNING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; SPEECH ANALYSIS; SUPPORT VECTOR MACHINE; SYSTEMATIC REVIEW (TOPIC); CLASSIFICATION; DATA MINING; DECISION SUPPORT SYSTEM; LITERATURE; MEDICAL RESEARCH; PROCEDURES; STATISTICAL MODEL;

BIOMEDICAL RESEARCH; DATA MINING; DECISION MAKING, COMPUTER-ASSISTED; HUMANS; MODELS, STATISTICAL; REVIEW LITERATURE AS TOPIC; SUPPORT VECTOR MACHINE;

EID: 84960101309     PISSN: None     EISSN: 20464053     Source Type: Journal    
DOI: 10.1186/s13643-015-0117-0     Document Type: Article

References (42)

1. Barza M, Trikalinos TA, Lau J. Statistical considerations in meta-analysis. Infect Dis Clin North Am. 2009; 23(2):195-210.
2. Counsell C. Formulating questions and locating primary studies for inclusion in systematic reviews. Ann Intern Med. 1997; 127(5):380-7.
3. Boudin F, Nie JY, Bartlett JC, Grad R, Pluye P, Dawes M. Combining classifiers for robust PICO element detection. BMC Med Inform Decis Mak. 2010; 10(1):29.
4. Demner-Fushman D, Lin J. Answering clinical questions with knowledge-based and statistical techniques. Comput Ling. 2007; 33(1):63-103.
5. Hunter L, Cohen KB. Biomedical language processing: perspective what's beyond PubMed?Mol Cell. 2006; 21(5):589.
6. Wallace BC, Small K, Brodley CE, Trikalinos TA. Active learning for biomedical citation screening. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM: 2010. p. 173-82.
7. Cohen AM. Performance of support-vector-machine-based classification on 15 systematic review topics evaluated with the WSS@95 measure. J Am Med Inform Assoc. 2011; 18(1):104-4.
8. Miwa M, Thomas J, O'Mara-Eves A, Ananiadou S. Reducing systematic review workload through certainty-based screening. J Biomed Inform. 2014; 51:242-53.
9. Ananiadou S, Rea B, Okazaki N, Procter R, Thomas J. Supporting systematic reviews using text mining. Syst Rev. 2015; 4(1):5.
10. O'Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Sys Rev. 2015; 4(1). doi: 10.1186/2046-4053-4-5. Highly Accessed.
11. García Adeva J, Pikatza Atxa J, Ubeda Carrillo M, Ansuategi Zengotitabengoa E. Automatic text classification to support systematic reviews in medicine. Expert Syst Appl. 2014; 41(4):1498-1508.
12. Wallace BC, Trikalinos TA, Lau J, Brodley C, Schmid CH. Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinf. 2010; 11(1):55.
13. Lukins SK, Kraft NA, Etzkorn LH. Source code retrieval for bug localization using latent Dirichlet allocation. In: Reverse Engineering, 2008. WCRE'08. 15th Working Conference On. Antwerp: IEEE: 2008. p. 155-64.
14. Henderson K, Eliassi-Rad T. Applying latent Dirichlet allocation to group discovery in large graphs. In: Proceedings of the 2009 ACM Symposium on Applied Computing. New York, NY, USA: ACM: 2009. p. 1456-1461.
15. Maskeri G, Sarkar S, Heafield K. Mining business topics in source code using latent Dirichlet allocation. In: Proceedings of the 1st India Software Engineering Conference. New York, NY, USA: ACM: 2008. p. 113-20.
16. Linstead E, Lopes C, Baldi P. An application of latent Dirichlet allocation to analyzing software evolution. In: Machine learning and applications, 2008. ICMLA'08. Seventh International Conference On. San Diego, CA: IEEE: 2008. p. 813-8.
17. Topic Model. http://en.wikipedia.org/wiki/Topic_model , accessed 1-Oct-2015.
18. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. Js Mach Learn Res. 2003; 3:993-1022.
19. Redner RA, Walker HF. Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev. 1984; 26(2):195-239.
20. Heinrich G. Parameter estimation for text analysis. 2005. Technical report. http://www.arbylon.net/publications/text-est.pdf. accessed 1-10-2015.
21. Berry MW, Dumais ST, O'Brien GW. Using linear algebra for intelligent information retrieval. SIAM review. 1995; 37(4):573-595.
22. Lee M, Mimno D. Low-dimensional embeddings for interpretable anchor-based topic inference. In: Proceedings of Empirical Methods in Natural Language Processing: 2014.
23. Arora S, Ge R, Moitra A. Learning topic models-going beyond SVD. In: Foundations of Computer Science (FOCS), 2012 IEEE 53rd Annual Symposium On. New Brunswick, NJ: IEEE: 2012. p. 1-10.
24. Bekhuis T, Demner-Fushman D. Towards automating the initial screening phase of a systematic review. Stud Health Technol Inform. 2010; 160(Pt 1):146-50.
25. Octaviano FR, Felizardo KR, Maldonado JC, Fabbri SC. Semi-automatic selection of primary studies in systematic literature reviews: is it reasonable?Empir Softw Eng. 2015; 20(6):1898-1917.
26. Cohen AM. Optimizing feature representation for automated systematic review work prioritization. In: AMIA Annual Symposium Proceedings: 2008. p. 121. American Medical Informatics Association.
27. Medical Subject Headings. https://www.nlm.nih.gov/pubs/factsheets/mesh.html , accessed 1-Oct-2015.
28. Romero Felizardo K, Souza SR, Maldonado JC. The use of visual text mining to support the study selection activity in systematic literature reviews: a replication study. In: Replication in empirical software engineering research (RESER), 2013 3rd International Workshop On. Baltimore, MD: IEEE: 2013. p. 91-100.
29. Fiszman M, Bray BE, Shin D, Kilicoglu H, Bennett GC, Bodenreider O, et al.Combining relevance assignment with quality of the evidence to support guideline development. Stud Health Technol Inform. 2010; 160(01):709.
30. Frunza O, Inkpen D, Matwin S. Building systematic reviews using automatic text classification techniques. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Beijing, China: Association for Computational Linguistics: 2010. p. 303-11.
31. Jonnalagadda S, Petitti D. A new iterative method to reduce workload in systematic review process. Int J Comput Biol Drug Des. 2013; 6(1):5-17.
32. Matwin S, Kouznetsov A, Inkpen D, Frunza O, O'Blenis P. A new algorithm for reducing the workload of experts in performing systematic reviews. J Am Med Inform Assoc. 2010; 17(4):446-53.
33. Cohen AM, Hersh WR, Peterson K, Yen PY. Reducing workload in systematic review preparation using automated citation classification. J Am Med Inform Assoc. 2006; 13(2):206-19.
34. Bekhuis T, Demner-Fushman D. Screening nonrandomized studies for medical systematic reviews: a comparative study of classifiers. Artif Intell Med. 2012; 55(3):197-207.
35. Bekhuis T, Tseytlin E, Mitchell KJ, Demner-Fushman D. Feature engineering and a proposed decision-support system for systematic reviewers of medical evidence. PloS one. 2014; 9(1):86277.
36. Frantzi K, Ananiadou S, Mima H. Automatic recognition of multi-word terms: the C-value/NC-value method. Int J Digit Libr. 2000; 3(2):115-30.
37. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST). 2011; 2(3):27.
38. Řehuřek R, Sojka P. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, Malta: ELRA: 2010. p. 45-50. http://is.muni.cz/publication/884893/en.
39. Kotsiantis S, Kanellopoulos D, Pintelas P. Handling imbalanced datasets: a review. GESTS Int Trans Comput Sci Eng. 2006; 30(1):25-36.
40. Steyvers M, Griffiths T. Matlab topic modeling tooklbox 1.3. 2005. http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm , accessed 1-Oct-2015.
41. EPPI-center. http://eppi.ioe.ac.uk/cms/ , accessed 1-Oct-2015.
42. Akbani R, Kwek S, Japkowicz N. Applying support vector machines to imbalanced datasets. In: Machine learning: ECML 2004. Berlin Heidelbeg: Springer: 2004. p. 39-50.