Abstract
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.
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References
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)
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)
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)
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)
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)
Blandford, A., Attfield, S.: Interacting with information. Synthesis Lectures on Human-Centered Informatics 3(1), 1–99 (2010)
Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data - Challenges in Human Computer Interaction & Biomedical Informatics (2012)
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)
Funk, P., Xiong, N.: Case-based reasoning and knowledge discovery in medical applications with time series. Computational Intelligence 22(3-4), 238–253 (2006)
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)
Waldrop, M.M.: Natural-language understanding. Science 224(4647), 372–374 (1984)
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)
Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)
Yndurain, E., Bernhardt, D., Campo, C.: Augmenting mobile search engines to leverage context awareness. IEEE Internet Computing 16(2), 17–25 (2012)
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)
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)
Zins, C.: Conceptual approaches for defining data, information, and knowledge: Research articles. J. Am. Soc. Inf. Sci. Technol. 58(4), 479–493 (2007)
Boisot, M., Canals, A.: Data, information and knowledge: have we got it right? IN3 Working Paper Series (4) (2004)
Mitkov, R.: The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics S.). Oxford University Press (2003)
Ferrucci, D., Lally, A.: Building an example application with the unstructured information management architecture. IBM Systems Journal 43(3), 455–475 (2004)
Nasukawa, T., Nagano, T.: Text analysis and knowledge mining system. IBM Systems Journal 40(4), 967–984 (2001)
Gotz, T., Suhre, O.: Design and implementation of the uima common analysis system. IBM Systems Journal 43(3), 476–489 (2004)
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)
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)
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)
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)
Clark, A., Fox, C., Lappin, S. (eds.): The Handbook of Computational Linguistics and Natural Language Processing. Blackwell Handbooks in Linguistics. John Wiley & Sons (2010)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)
Schmid, H.: Probabilistic part-of-speech tagging using decision trees (1994)
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Holzinger, A., Stocker, C., Ofner, B., Prohaska, G., Brabenetz, A., Hofmann-Wellenhof, R. (2013). Combining HCI, Natural Language Processing, and Knowledge Discovery - Potential of IBM Content Analytics as an Assistive Technology in the Biomedical Field. In: Holzinger, A., Pasi, G. (eds) Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. HCI-KDD 2013. Lecture Notes in Computer Science, vol 7947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39146-0_2
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DOI: https://doi.org/10.1007/978-3-642-39146-0_2
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