Reducing systematic review workload through certainty-based screening

Journal of Biomedical Informatics

Volumn 51, Issue , 2014, Pages 242-253

  Miwa, Makoto ^a,b   Thomas, James ^c   O'Mara Eves, Alison ^c   Ananiadou, Sophia ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b TOYOTA TECHNOLOGICAL INSTITUTE   (Japan)

^c UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Active learning; Certainty; Systematic reviews; Text mining

Indexed keywords

BEHAVIORAL RESEARCH; CLASSIFICATION (OF INFORMATION); DIAGNOSIS; STATISTICS; TEXT MINING;

ACTIVE LEARNING; CERTAINTY; LATENT DIRICHLET ALLOCATIONS; POSITIVE INSTANCES; RELEVANT DOCUMENTS; SYSTEMATIC REVIEW; TEXT CLASSIFICATION; UNSUPERVISED TECHNIQUES;

LEARNING SYSTEMS;

ARTICLE; CLASSIFICATION; CLINICAL MEDICINE; DATA BASE; DATA MINING; HUMAN; LEARNING; SCREENING; SOCIOLOGY; SUPPORT VECTOR MACHINE; SYSTEMATIC REVIEW (TOPIC); UNCERTAINTY; WORKLOAD; ALGORITHM; ARTIFICIAL INTELLIGENCE; BIBLIOGRAPHIC DATABASE; DOCUMENTATION; LITERATURE; NATURAL LANGUAGE PROCESSING; PEER REVIEW; PROCEDURES; PUBLICATION; SEMANTICS;

ABSTRACTING AND INDEXING AS TOPIC; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASES, BIBLIOGRAPHIC; MANUSCRIPTS AS TOPIC; NATURAL LANGUAGE PROCESSING; PEER REVIEW, RESEARCH; REVIEW LITERATURE AS TOPIC; SEMANTICS; WORKLOAD;

EID: 84908023692     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.06.005     Document Type: Article

References (24)

1. Chalmers I., Hedges L.V., Cooper H. A brief history of research synthesis. Eval Health Prof 2002, 25(1):12-37.
2. Mulrow C.D. Rationale for systematic reviews. BMJ: Brit Med J 1994, 309(6954):597.
3. Lefebvre C, Manheimer E, Glanville J. Cochrane handbook for systematic reviews of interventions, version 5.1.0 edition, chap. 6; 2011.
4. Wallace B., Trikalinos T., Lau J., Brodley C., Schmid C. Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinform 2010, 11(1):55.
5. Ananiadou S., Rea B., Okazaki N., Procter R., Thomas J. Supporting systematic reviews using text mining. Soc Sci Comput Rev 2009, 27(4):509-523.
6. Thomas J., McNaught J., Ananiadou S. Applications of text mining within systematic reviews. Res Synth Methods 2011, 2(1):1-14.
7. Tsafnat G., Dunn A., Glasziou P. Coiera E. The automation of systematic reviews, BMJ: Brit Med J 2013, 346:139.
8. Tsuruoka Y., Miwa M., Hamamoto K., Tsujii J., Ananiadou S. Discovering and visualizing indirect associations between biomedical concepts. Bioinformatics 2011, 27(13):i111-i119.
9. Ananiadou S., McNaught J. Text mining for biology and biomedicine 2006, Artech House, Boston/London.
10. Sasaki Y., Rea B., Ananiadou S. Clinical text classification under the open and closed topic assumptions. Int J Data Mining Bioinform 2009, 3(3):299-313.
11. Wallace B.C., Small K., Brodley C.E., Lau J., Schmid C.H., Bertram L., et al. Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining. Genet Med 2012, 14(7):663-669.
12. Shemilt I., Simon A., Hollands G.J., Marteau T.M., Ogilvie D., O'Mara-Eves A., et al. Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews. Res Synth Methods 2014, 5(1):31-49.
13. 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.
14. Tanja B., Dina D.-F. Towards automating the initial screening phase of a systematic review. Stud Health Technol Inform 2010, 160(1):146-150.
15. Cohen A.M. Performance of support-vector-machine-based classification on 15 systematic review topics evaluated with the wss@95 measure. JAMIA: J Am Med Inform Assoc 2011, 18(1):104.
16. Settles B. Active learning literature survey. Computer sciences technical report 1648. University of Wisconsin-Madison; 2009.
17. Ganti R., Gray A.G. Upal: unbiased pool based active learning. J Mach Learn Res - Proc Track 2012, 22:422-431.
18. Cortes C., Vapnik V. Support-vector networks. Mach Learn 1995, 20(3):273-297.
19. Fu J.H., Lee S.L. Certainty-enhanced active learning for improving imbalanced data classification. Proceedings of the 2011 IEEE 11th international conference on data mining workshops, ICDMW '11 2011, 405-412. IEEE Computer Society, Washington, DC (USA).
20. Bickel S., Brückner M., Scheffer T. Discriminative learning under covariate shift. J Mach Learn Res 2009, 10:2137-2155.
21. Blei D.M., Ng A.Y., Jordan M.I. Latent dirichlet allocation. J Mach Learn Res 2003, 3:993-1022.
22. Fan R.-E., Chang K.-W., Hsieh C.-J., Wang X.-R., Lin C.-J. Liblinear: a library for large linear classification. J Mach Learn Res 2008, 9:1871-1874.
23. Řehůřek R, Sojka P. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. ELRA, Valletta, Malta; 2010. p. 45-50.
24. Wallace B.C., Small K., Brodley C.E., Trikalinos T.A. Active learning for biomedical citation screening. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10 2010, 173-182. ACM, New York (NY, USA).