Combining HCI, Natural Language Processing, and Knowledge Discovery - Potential of IBM Content Analytics as an Assistive Technology in the Biomedical Field

  • Andreas Holzinger
  • Christof Stocker
  • Bernhard Ofner
  • Gottfried Prohaska
  • Alberto Brabenetz
  • Rainer Hofmann-Wellenhof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7947)


Medical professionals are confronted with a flood of big data most of it containing unstructured information. Such unstructured information is the subset of information, where the information itself describes parts of what constitutes as significant within it, or in other words - structure and information are not completely separable. The best example for such unstructured information is text. For many years, text mining has been an essential area of medical informatics. Although text can easily be created by medical professionals, the support of automatic analyses for knowledge discovery is extremely difficult. We follow the definition that knowledge consists of a set of hypotheses, and knowledge discovery is the process of finding or generating new hypotheses by medical professionals with the aim of getting insight into the data. In this paper we present some lessons learned of ICA for dermatological knowledge discovery, for the first time. We follow the HCI-KDD approach, i.e. with the human expert in the loop matching the best of two worlds: human intelligence with computational intelligence.


Knowledge discovery data mining human-computer interaction medical informatics Unstructured Information Management Content Analytics 


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  1. 1.
    Holzinger, A., Geierhofer, R., Modritscher, F., Tatzl, R.: Semantic information in medical information systems: Utilization of text mining techniques to analyze medical diagnoses. Journal of Universal Computer Science 14(22), 3781–3795 (2008)Google Scholar
  2. 2.
    Kreuzthaler, M., Bloice, M., Faulstich, L., Simonic, K., Holzinger, A.: A comparison of different retrieval strategies working on medical free texts. Journal of Universal Computer Science 17(7), 1109–1133 (2011)Google Scholar
  3. 3.
    Gregory, J., Mattison, J.E., Linde, C.: Naming notes - transitions from free-text to structured entry. Methods of Information in Medicine 34(1-2), 57–67 (1995)Google Scholar
  4. 4.
    Holzinger, A., Kainz, A., Gell, G., Brunold, M., Maurer, H.: Interactive computer assisted formulation of retrieval requests for a medical information system using an intelligent tutoring system. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 431–436. AACE, Charlottesville (2000)Google Scholar
  5. 5.
    Lovis, C., Baud, R.H., Planche, P.: Power of expression in the electronic patient record: structured data or narrative text? International Journal of Medical Informatics 58, 101–110 (2000)CrossRefGoogle Scholar
  6. 6.
    Blandford, A., Attfield, S.: Interacting with information. Synthesis Lectures on Human-Centered Informatics 3(1), 1–99 (2010)CrossRefGoogle Scholar
  7. 7.
    Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data - Challenges in Human Computer Interaction & Biomedical Informatics (2012)Google Scholar
  8. 8.
    Beale, R.: Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and web browsing. International Journal of Human-Computer Studies 65(5), 421–433 (2007)CrossRefGoogle Scholar
  9. 9.
    Funk, P., Xiong, N.: Case-based reasoning and knowledge discovery in medical applications with time series. Computational Intelligence 22(3-4), 238–253 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Holzinger, A., Scherer, R., Seeber, M., Wagner, J., Müller-Putz, G.: Computational Sensemaking on Examples of Knowledge Discovery from Neuroscience Data: Towards Enhancing Stroke Rehabilitation. In: Böhm, C., Khuri, S., Lhotská, L., Renda, M.E. (eds.) ITBAM 2012. LNCS, vol. 7451, pp. 166–168. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Waldrop, M.M.: Natural-language understanding. Science 224(4647), 372–374 (1984)CrossRefGoogle Scholar
  12. 12.
    Weizenbaum, J.: Eliza - a computer program for study of natural language communication between man and machine. Communications of the ACM 9(1), 36–45 (1966)CrossRefGoogle Scholar
  13. 13.
    Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Yndurain, E., Bernhardt, D., Campo, C.: Augmenting mobile search engines to leverage context awareness. IEEE Internet Computing 16(2), 17–25 (2012)CrossRefGoogle Scholar
  15. 15.
    Erhardt, R.A.A., Schneider, R., Blaschke, C.: Status of text-mining techniques applied to biomedical text. Drug Discovery Today 11(7-8), 315–325 (2006)CrossRefGoogle Scholar
  16. 16.
    Lee, W.B., Wang, Y., Wang, W.M., Cheung, C.F.: An unstructured information management system (uims) for emergency management. Expert Systems with Applications 39(17), 12743–12758 (2012)CrossRefGoogle Scholar
  17. 17.
    Zins, C.: Conceptual approaches for defining data, information, and knowledge: Research articles. J. Am. Soc. Inf. Sci. Technol. 58(4), 479–493 (2007)CrossRefGoogle Scholar
  18. 18.
    Boisot, M., Canals, A.: Data, information and knowledge: have we got it right? IN3 Working Paper Series (4) (2004)Google Scholar
  19. 19.
    Mitkov, R.: The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics S.). Oxford University Press (2003)Google Scholar
  20. 20.
    Ferrucci, D., Lally, A.: Building an example application with the unstructured information management architecture. IBM Systems Journal 43(3), 455–475 (2004)CrossRefGoogle Scholar
  21. 21.
    Nasukawa, T., Nagano, T.: Text analysis and knowledge mining system. IBM Systems Journal 40(4), 967–984 (2001)CrossRefGoogle Scholar
  22. 22.
    Gotz, T., Suhre, O.: Design and implementation of the uima common analysis system. IBM Systems Journal 43(3), 476–489 (2004)CrossRefGoogle Scholar
  23. 23.
    Mack, R., Mukherjea, S., Soffer, A., Uramoto, N., Brown, E., Coden, A., Cooper, J., Inokuchi, A., Iyer, B., Mass, Y., Matsuzawa, H., Subramaniam, L.V.: Text analytics for life science using the unstructured information management architecture. IBM Systems Journal 43(3), 490–515 (2004)CrossRefGoogle Scholar
  24. 24.
    Holzinger, A., Simonic, K., Yildirim, P.: Disease-disease relationships for rheumatic diseases: Web-based biomedical textmining and knowledge discovery to assist medical decision making (2012)Google Scholar
  25. 25.
    Holzinger, A., Yildirim, P., Geier, M., Simonic, K.-M.: Quality-based knowledge discovery from medical text on the Web Example of computational methods in Web intelligence. In: Pasi, G., Bordogna, G., Jain, L.C. (eds.) Qual. Issues in the Management of Web Information. ISRL, vol. 50, pp. 145–158. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  26. 26.
    Garvin, J.H., DuVall, S.L., South, B.R., Bray, B.E., Bolton, D., Heavirland, J., Pickard, S., Heidenreich, P., Shen, S.Y., Weir, C., Samore, M., Goldstein, M.K.: Automated extraction of ejection fraction for quality measurement using regular expressions in unstructured information management architecture (uima) for heart failure. Journal of the American Medical Informatics Association 19(5), 859–866 (2012)CrossRefGoogle Scholar
  27. 27.
    Clark, A., Fox, C., Lappin, S. (eds.): The Handbook of Computational Linguistics and Natural Language Processing. Blackwell Handbooks in Linguistics. John Wiley & Sons (2010)Google Scholar
  28. 28.
    Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  29. 29.
    Schmid, H.: Probabilistic part-of-speech tagging using decision trees (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Christof Stocker
    • 1
  • Bernhard Ofner
    • 1
  • Gottfried Prohaska
    • 2
  • Alberto Brabenetz
    • 2
  • Rainer Hofmann-Wellenhof
    • 3
  1. 1.Institute for Medical Informatics, Statistics & Documentation, Research Unit HCI4MEDMedical University GrazGrazAustria
  2. 2.IBM AustriaViennaAustria
  3. 3.Department for DermatologyLKH-University Hospital GrazGrazAustria

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