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

  • Joël Kuiper
  • Gert van Valkenhoef
Conference paper

DOI: 10.1007/978-3-642-38326-7_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)
Cite this paper as:
Kuiper J., van Valkenhoef G. (2013) Top-Level MeSH Disease Terms Are Not Linearly Separable in Clinical Trial Abstracts. In: Peek N., Marín Morales R., Peleg M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science, vol 7885. Springer, Berlin, Heidelberg

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations