Spá: A Web-Based Viewer for Text Mining in Evidence Based Medicine

  • J. Kuiper
  • I. J. Marshall
  • B. C. Wallace
  • M. A. Swertz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)


Summarizing the evidence about medical interventions is an immense undertaking, in part because unstructured Portable Document Format (PDF) documents remain the main vehicle for disseminating scientific findings. Clinicians and researchers must therefore manually extract and synthesise information from these PDFs. We introduce Spá1,2 a web-based viewer that enables automated annotation and summarisation of PDFs via machine learning. To illustrate its functionality, we use Spá to semi-automate the assessment of bias in clinical trials. Spá has a modular architecture, therefore the tool may be widely useful in other domains with a PDF-based literature, including law, physics, and biology


Computational Linguistics Portable Document Format Document Level Main Vehicle British Medical Jour 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kuiper, J., Wallace, B.C., Marshall, I.J.: Spa (2014),
  2. 2.
    Sackett, D.L., Rosenberg, W.M., Gray, J., Haynes, R.B., Richardson, W.S.: Evidence based medicine: what it is and what it isn’t. BMJ: British Medical Journal 312(7023), 71–72 (1996)CrossRefGoogle Scholar
  3. 3.
    Valkenhoef, G., Tervonen, T., Brock, B., Hillege, H.: Deficiencies in the transfer and availability of clinical trials evidence: a review of existing systems and standards. BMC Medical Informatics and Decision Making 12(1), 95 (2012)Google Scholar
  4. 4.
    Higgins, J., Altman, D., Gotzsche, P., Juni, P., Moher, D., Oxman, A., Savovic, J., Schulz, K., Weeks, L., Sterne, J.: The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343, d5928 (2011)Google Scholar
  5. 5.
    Hartling, L., Bond, K., Vandermeer, B., Seida, J., Dryden, D.M., Rowe, B.H.: Applying the risk of bias tool in a systematic review of combination long-acting beta-agonists and inhaled corticosteroids for persistent asthma. PloS one 6(2), e17242 (2011)Google Scholar
  6. 6.
    Hartling, L., Ospina, M., Liang, Y.: Risk of bias versus quality assessment of randomised controlled trials: cross sectional study. BMJ 339, b4012 (2009)Google Scholar
  7. 7.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL, Association for Computational Linguistics, pp. 1003–1011 (2009)Google Scholar
  8. 8.
    Nguyen, T., Moschitti, A.: End-to-end relation extraction using distant supervision from external semantic repositories. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 277–282 (2011)Google Scholar
  9. 9.
    Evgeniou, T., Pontil, M.: Regularized multi–task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 109–117. ACM, New York (2004)CrossRefGoogle Scholar
  10. 10.
    Daumé III, H.: Frustratingly easy domain adaptation. In: Association for Computatoinal Linguistics (ACL), vol. 1785 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • J. Kuiper
    • 1
  • I. J. Marshall
    • 2
  • B. C. Wallace
    • 3
  • M. A. Swertz
    • 1
  1. 1.University of GroningenGroningenThe Netherlands
  2. 2.King’s College LondonLondonUK
  3. 3.University of Texas at AustinAustinUSA

Personalised recommendations