Learning to identify relevant studies for systematic reviews using random forest and external information

Machine Learning

Volumn 102, Issue 3, 2016, Pages 465-482

  Khabsa, Madian ^a   Elmagarmid, Ahmed ^b   Ilyas, Ihab ^c   Hammady, Hossam ^b   Ouzzani, Mourad ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

^c UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Classification; Inclusion prediction; Systematic review

Indexed keywords

DECISION TREES; HEURISTIC METHODS;

EXTERNAL INFORMATIONS; HEALTH CARE PROFESSIONALS; IMBALANCED DATA; LEXICAL FEATURES; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; SYSTEMATIC REVIEW; WORD CLUSTERING;

CLASSIFICATION (OF INFORMATION);

EID: 84959537001     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-015-5535-7     Document Type: Article

References (26)

1. Bastian, H., Glasziou, P., & Chalmers, I. (2010). Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLoS Medicine, 7(9), e1000,326.
2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
3. Breiman, L. (2002). Manual on setting up, using, and understanding random forests v3. 1. http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.
4. Brown, P. F., Desouza, P. V., Mercer, R. L., Pietra, V. J. D., & Lai, J. C. (1992). Class-based n-gram models of natural language. Computational Linguistics, 18(4), 467–479.
5. Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on machine learning, pp. 161–168. ACM, New York.
6. Chen, C., Liaw, A., & Breiman, L. (2004). Using random forest to learn imbalanced data. Berkeley: University of California.
7. Cohen, A. M. (2008). Optimizing feature representation for automated systematic review work prioritization. In AMIA annual symposium proceedings, vol. 2008, p. 121. American Medical Informatics Association, New York
8. Cohen, A. M. (2011). Performance of support-vector-machine-based classification on 15 systematic review topics evaluated with the wss@ 95 measure. Journal of the American Medical Informatics Association, 18(1), 104–104.
9. Cohen, A. M., Ambert, K., & McDonagh, M. (2012). Studying the potential impact of automated document classification on scheduling a systematic review update. BMC Medical Informatics and Decision Making, 12(1), 33.
10. Cohen, A. M., Hersh, W. R., Peterson, K., & Yen, P. Y. (2006). Reducing workload in systematic review preparation using automated citation classification. Journal of the American Medical Informatics Association, 13(2), 206–219.
11. Cook, D. J., Mulrow, C. D., & Haynes, R. B. (1997). Systematic reviews: Synthesis of best evidence for clinical decisions. Annals of Internal Medicine, 126(5), 376–380.
12. Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 155–164. ACM, New York
13. Duda, R. O., Hart, P. E., et al. (1973). Pattern classification and scene analysis (Vol. 3). New York: Wiley.
14. Freund, Y., Schapire, R.E., et al. (1996). Experiments with a new boosting algorithm. In ICML, vol. 96, pp. 148–156
15. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The weka data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.
16. Jonnalagadda, S., & Petitti, D. (2013). A new iterative method to reduce workload in systematic review process. International Journal of Computational Biology and Drug Design, 6(1), 5–17.
17. Kouznetsov, A., Matwin, S., Inkpen, D., Razavi, A. H., Frunza, O., Sehatkar, M., Seaward, L., & OBlenis, P. (2009). Classifying biomedical abstracts using committees of classifiers and collective ranking techniques. In Advances in artificial intelligence, pp. 224–228. Berlin: Springer
18. Matwin, S., Kouznetsov, A., Inkpen, D., Frunza, O., & O’Blenis, P. (2010). A new algorithm for reducing the workload of experts in performing systematic reviews. Journal of the American Medical Informatics Association, 17(4), 446–453.
19. Miwa, M., Thomas, J., OMara-Eves, A., & Ananiadou, S. (2014). Reducing systematic review workload through. Journal of Biomedical Informatics, 51, 242–253.
20. Raeder, T., Hoens, T. R., & Chawla, N. V. (2010). Consequences of variability in classifier performance estimates. In IEEE 10th international conference on data mining (ICDM), 2010, pp. 421–430. IEEE.
21. Shojania, K. G., Sampson, M., Ansari, M. T., Ji, J., Doucette, S., & Moher, D. (2007). How quickly do systematic reviews go out of date? A survival analysis. Annals of Internal Medicine, 147(4), 224–233.
22. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265–269.
23. Tomassetti, F., Rizzo, G., Vetro, A., Ardito, L., Torchiano, M., & Morisio, M. (2011). Linked data approach for selection process automation in systematic reviews. In 15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011), pp. 31–35. IET.
24. Wallace, B. C., Small, K., Brodley, C. E., & Trikalinos, T. A. (2010). Active learning for biomedical citation screening. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 173–182. ACM, New York
25. Wallace, B. C., Small, K., Brodley, C. E., & Trikalinos, T. A. (2011). Who should label what? Instance allocation in multiple expert active learning. In SDM, pp. 176–187.
26. Wallace, B. C., Trikalinos, T. A., Lau, J., Brodley, C., & Schmid, C. H. (2010). Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinformatics, 11(1), 55.