Top-Level MeSH Disease Terms Are Not Linearly Separable in Clinical Trial Abstracts

  • Joël Kuiper
  • Gert van Valkenhoef
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)


Assessments of the efficacy and safety of medical interventions are based on systematic reviews of clinical trials. Systematic reviewing requires the screening of vast amounts of publications, which is currently done by hand. To reduce the number of publications that are screened manually, we propose the automated classification of publications by disease category using Support Vector Machines. We base our classification on the ontological structure of the (MeSH) by treating all terms as their top-level disease category. Unfortunately the resulting classifier lacks sufficient sensitivity for use by systematic reviewers. We argue that this is partially due to the inseparability of the terminology into the disease categories and discuss how future work could address this problem.


Latent Dirichlet Allocation Latent Semantic Analysis Ontological Structure Systematic Reviewer Immune System Disease 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Evidence-Based Medicine Working Group: Evidence-based medicine. A new approach to teaching the practice of medicine. Journal of the American Medical Association 268(17), 2420–2425 (1992) Google Scholar
  2. 2.
    van Valkenhoef, G., Tervonen, T., de Brock, B., Hillege, H.: Deficiencies in the transfer and availability of clinical evidence in drug development and regulation. BMC Medical Informatics and Decision Making (2012) (in press)Google Scholar
  3. 3.
    Haynes, R.B., McKibbon, K.A., Wilczynski, N.L., Walter, S.D., Werre, S.R.: Optimal search strategies for retrieving scientifically strong studies of treatment from Medline: analytical survey. BMJ 330(7501), 1179 (2005)CrossRefGoogle Scholar
  4. 4.
    Higgins, J., Green, S. (eds.): Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.2. The Cochrane Collaboration (2009), (updated September 2009)
  5. 5.
    Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L.J., Eilbeck, K., Ireland, A., Mungall, C.J., Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.A., Scheuermann, R.H., Shah, N., Whetzel, P.L., Lewis, S.: The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25(11), 1251–1255 (2007)CrossRefGoogle Scholar
  6. 6.
    Adamusiak, T., Burdett, T., Kurbatova, N., van der Velde, K.J., Abeygunawardena, N., Antonakaki, D., Kapushesky, M., Parkinson, H., Swertz, M.A.: OntoCAT – simple ontology search and integration in java, r and REST/JavaScript. BMC Bioinformatics 12(1), 218 (2011)CrossRefGoogle Scholar
  7. 7.
    Krovetz, R.: Viewing morphology as an inference process. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1993, pp. 191–202. ACM, New York (1993)Google Scholar
  8. 8.
    Salton, G., Fox, E.A., Wu, H.: Extended boolean information retrieval. Commun. ACM 26(11), 1022–1036 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Joachims, T.: Text categorization with suport vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Joachims, T.: Training linear SVMs in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226 (2006)Google Scholar
  11. 11.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)zbMATHGoogle Scholar
  12. 12.
    Dumais, S.T., Furnas, G.W., Landauer, T.K., Deerwester, S., Harshman, R.: Using latent semantic analysis to improve access to textual information. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1988, pp. 281–285. ACM, New York (1988)Google Scholar
  13. 13.
    Blei, D.M.: Probabilistic topic models. Communications of the ACM 55(4), 77–84 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Joël Kuiper
    • 1
  • Gert van Valkenhoef
    • 1
    • 2
  1. 1.Faculty of Economics and BusinessUniversity of GroningenGroningenThe Netherlands
  2. 2.Department of Epidemiology, University Medical Center GroningenUniversity of GroningenGroningenThe Netherlands

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