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A Domain-Expert Centered Process Model for Knowledge Discovery in Medical Research: Putting the Expert-in-the-Loop

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9250))

Abstract

Established process models for knowledge discovery see the domain expert in a customer-like, supervising role. In the field of bio-medical research, it is necessary for the domain experts to move into the center of this process with far-reaching consequences for their research work but also for the process itself. We revise the established process models for knowledge discovery and propose a new process model for domain-expert driven knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and show how the domain expert can be deeply integrated even into the highly complex data mining and machine learning tasks.

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References

  1. Anderson, N.R., Lee, E.S., Brockenbrough, J.S., Minie, M.E., Fuller, S., Brinkley, J., Tarczy-Hornoch, P.: Issues in biomedical research data management and analysis: Needs and barriers. Journal of the American Medical Informatics Association 14(4), 478–488 (2007). http://jamia.bmj.com/content/14/4/478.abstract

  2. Baigent, C., Harrell, F.E., Buyse, M., Emberson, J.R., Altman, D.G.: Ensuring trial validity by data quality assurance and diversification of monitoring methods. Clinical Trials 5(1), 49–55 (2008). http://ctj.sagepub.com/content/5/1/49.abstract

  3. Bellazzi, R., Zupan, B.: Predictive data mining in clinical medicine: current issues and guidelines. International Journal of Medical Informatics 77(2), 81–97 (2008)

    Article  Google Scholar 

  4. Van den Broeck, J., Cunningham, S.A., Eeckels, R., Herbst, K.: Data cleaning: detecting, diagnosing, and editing data abnormalities. PLoS Medicine 2(10), e267 (2005)

    Article  Google Scholar 

  5. Bursa, M., Lhotska, L., Chudacek, V., Spilka, J., Janku, P., Huser, M.: Practical Problems and Solutions in Hospital Information System Data Mining. In: Böhm, C., Khuri, S., Lhotská, L., Renda, M.E. (eds.) ITBAM 2012. LNCS, vol. 7451, pp. 31–39. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  6. Cios, K.J., Teresinska, A., Konieczna, S., Potocka, J., Sharma, S.: Diagnosing myocardial perfusion from pect bull-eye maps-a knowledge discovery approach. IEEE Engineering in Medicine and Biology Magazine 19(4), 17–25 (2000)

    Article  Google Scholar 

  7. Cios, K.J., William Moore, G.: Uniqueness of medical data mining. Artificial Intelligence in Medicine 26(1), 1–24 (2002)

    Google Scholar 

  8. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM 39(11), 27–34 (1996)

    Article  Google Scholar 

  9. Fayyad, U., Piatetsky-shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magazine 17, 37–54 (1996)

    Google Scholar 

  10. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in knowledge discovery and data mining (1996)

    Google Scholar 

  11. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2), 179–188 (1936)

    Article  Google Scholar 

  12. Franklin, J.D., Guidry, A., Brinkley, J.F.: A partnership approach for electronic data capture in small-scale clinical trials. Journal of Biomedical Informatics 44(suppl. 1), S103–S108 (2011)

    Google Scholar 

  13. Girardi, D., Arthofer, K.: An ontology-based data acquisition infrastructure - using ontologies to create domain-independent software systems. In: KEOD 2012, Proceedings of the International Conference on Knowledge Engineering and Ontology Development, Barcelona, Spain, October, 4-7, pp. 155–160. SciTePress, Barcelona (2012)

    Google Scholar 

  14. Girardi, D., Dirnberger, J., Trenkler, J.: A meta model-based web framework for domain independent data acquisition. In: ICCGI 2013, The Eighth International Multi-Conference on Computing in the Global Information Technology, pp. 133–138. International Academy, Research, and Industry Association, Nice, France (2013)

    Google Scholar 

  15. Girardi, D., Küng, J., Giretzlehner, M.: A Meta-model Guided Expression Engine. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014, Part I. LNCS, vol. 8397, pp. 1–10. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  16. Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data-challenges in human-computer interaction & biomedical informatics. In: DATA (2012)

    Google Scholar 

  17. Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge discovery and interactive data mining in bioinformatics - state-of-the-art, future challenges and research directions. BMC Bioinformatics 15(S6), I1 (2014). http://www.biomedcentral.com/1471-2105/15/S6/I1

  18. Holzinger, A., Jurisica, I.: Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. LNCS, vol. 8401, pp. 1–18. Springer, Heidelberg (2014)

    Google Scholar 

  19. Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)

    Google Scholar 

  20. Kieseberg, P., Schantl, J., Frhwirt, P., Weippl, E., Holzinger, A.: Witnesses for the doctor in the loop. In: Brain and Health Informatics BIH 2015, Lecture Notes in Artificial Intelligence LNAI. Springer, Heidelberg (in print, 2015)

    Google Scholar 

  21. Kurgan, L.A., Musilek, P.: A survey of knowledge discovery and data mining process models. The Knowledge Engineering Review 21(01), 1–24 (2006)

    Article  Google Scholar 

  22. Leiner, F., Gaus, W., Haux, R., Knaup-Gregori, P.: Medical Data Management - A Practical Guide. Springer (2003)

    Google Scholar 

  23. Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  24. Mariscal, G., Marbán, Ó., Fernández, C.: A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review 25(2), 137–166 (2010)

    Article  Google Scholar 

  25. Mirchevska, V., Lustrek, M., Gams, M.: Combining domain knowledge and machine learning for robust fall detection. Expert Systems 31(2), 163–175 (2014)

    Article  Google Scholar 

  26. Niakšu, O., Kurasova, O.: Data mining applications in healthcare: Research vs practice. Databases and Information Systems Baltic DB&IS 2012, p. 58 (2012)

    Google Scholar 

  27. Pal, N.R., Jain, L.: Advanced techniques in knowledge discovery and data mining. Springer, New York (2004)

    Google Scholar 

  28. Prokosch, H.U., Ganslandt, T.: Perspectives for medical informatics. Methods Inf. Med. 48(1), 38–44 (2009)

    Google Scholar 

  29. Roddick, J.F., Fule, P., Graco, W.J.: Exploratory medical knowledge discovery: experiences and issues. SIGKDD Explor. Newsl. 5(1), 94–99 (2003). http://doi.acm.org/10.1145/959242.959243

  30. Shearer, C.: The crisp-dm model: the new blueprint for data mining. Journal of Data Warehousing 5(4), 13–22 (2000)

    Google Scholar 

  31. Tsumoto, S., Hirano, S.: Data mining in hospital information system for hospital management. In: ICME International Conference on Complex Medical Engineering, CME 2009, pp. 1–5 (April 2009)

    Google Scholar 

  32. Tsumoto, S., Hirano, S., Tsumoto, Y.: Information reuse in hospital information systems: A data mining approach. In: 2011 IEEE International Conference on Information Reuse and Integration (IRI), pp. 172–176 (August 2011)

    Google Scholar 

  33. Webb, G.I.: Integrating machine learning with knowledge acquisition through direct interaction with domain experts. Knowledge-Based Systems 9(4), 253–266 (1996)

    Article  Google Scholar 

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Correspondence to Dominic Girardi .

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Girardi, D., Kueng, J., Holzinger, A. (2015). A Domain-Expert Centered Process Model for Knowledge Discovery in Medical Research: Putting the Expert-in-the-Loop. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-23344-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

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