Abstract
Biomedical research requires deep domain expertise to perform analyses of complex data sets, assisted by mathematical expertise provided by data scientists who design and develop sophisticated methods and tools. Such methods and tools not only require preprocessing of the data, but most of all a meaningful input selection. Usually, data scientists do not have sufficient background knowledge about the origin of the data and the biomedical problems to be solved, consequently a doctor-in-the-loop can be of great help here. In this paper we revise the viability of integrating an analysis guided visualization component in an ontology-guided data infrastructure, exemplified by the principal component analysis. We evaluated this approach by examining the potential for intelligent support of medical experts on the case of cerebral aneurysms research.
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References
Akgul, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: Current status and future directions. J. Digit. Imaging 24(2), 208–222 (2011)
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. J. Am. Med. Inf. Assoc. 14(4), 478–488 (2007)
Atzmüller, M., Baumeister, J., Puppe, F.: Introspective subgroup analysis for interactive knowledge refinement. In: Sutcliffe, G., Goebel, R. (eds.) FLAIRS Nineteenth International Florida Artificial Intelligence Research Society Conference, pp. 402–407. AAAI Press, Menlo Park (2006)
Buchan, I.E., Winn, J.M., Bishop, C.M.: A unified modeling approach to data-intensive healthcare. In: Hey, T., Tansley, S., Tolle, K. (eds.) The fourth paradigm: Data-Intensive Scientific Discovery, pp. 91–98. Microsoft Research, Redmond (2009)
Cios, K.J., William Moore, G.: Uniqueness of medical data mining. Artif. Intell. Med. 26(1), 1–24 (2002)
Gigerenzer, G., Gaissmaier, W.: Heuristic decision making. Ann. Rev. Psychol. 62, 451–482 (2011)
Girardi, D., Dirnberger, J., Giretzlehner, M.: An ontology-based clinical data warehouse for scientific research. Saf. Health 1(1), 1–9 (2015)
Girardi, D., Kueng, J., Holzinger, A.: 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.) BIH 2015. LNCS, vol. 9250, pp. 389–398. Springer, Heidelberg (2015)
Girardi, D., Küng, J., Kleiser, R., Sonnberger, M., Csillag, D., Trenkler, J., Holzinger, A.: Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research. Brain Inf., 1–11 (2016). (Online First Articles)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Holzinger, A.: Human-computer interaction and knowledge discovery (HCI-KDD): what is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013)
Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: Cognitive science meets machine learning. IEEE Intell. Inf. Bull. 15(1), 6–14 (2014)
Holzinger, A.: Interactive machine learning for health informatics: When do we need the human-in-the-loop? Springer Brain Inform. (BRIN) 3, 1–13 (2016). http://dx.doi.org/10.1007/s40708-016-0042-6
Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge discovery and interactive data mining in bioinformatics - state-of-the-art, future challenges and research directions. BMC Bioinform. 15(S6), I1 (2014)
Holzinger, Andreas, Stocker, Christof, Dehmer, Matthias: Big complex biomedical data: towards a taxonomy of data. In: Obaidat, Mohammad S., Filipe, Joaquim (eds.) ICETE 2012. CCIS, vol. 455, pp. 3–18. Springer, Heidelberg (2014)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933)
Hund, M., Bhm, D., Sturm, W., Sedlmair, M., Schreck, T., Ullrich, T., Keim, D.A., Majnaric, L., Holzinger, A.: Visual analytics for concept exploration in subspaces of patient groups: Making sense of complex datasets with the doctor-in-the-loop. Brain Inf. 3, 1–15 (2016)
Kessler, W.: Multivariate Datenanalyse: für die Pharma-Bio- und Prozessanalytik. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim (2007)
Kurgan, L.A., Musilek, P.: A survey of knowledge discovery and data mining process models. Knowl. Eng. Rev. 21(01), 1–24 (2006)
Malinowski, E.: A thesis in two parts: application of factor analysis to chemical problems. Stevens Inst. Technol. 2(1–2), 54–94 (1961)
Nandi, D., Ashour, A.S., Samanta, S., Chakraborty, S., Salem, M.A., Dey, N.: Principal component analysis in medical image processing: a study. Int. J. Image Min. 1(1), 65–86 (2015)
National Center for Biotechnology Information: Mesh search for principalcomponent analysis and medicine (2016). http://www.ncbi.nlm.nih.gov/
Niakšu, O., Kurasova, O.: Data mining applications in healthcare: research vs practice. Databases Inf. Syst. BalticDB&IS 2012, 58 (2012)
NIH: Cerebral Aneurysm Information Page (April 2010). http://www.ninds.nih.gov/disorders/cerebral_aneurysm/cerebral_aneurysm.htm
Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 559–572 (1901)
Rencher, A.: Methods of Multivariate Analysis. Wiley Series in Probability and Statistics. Wiley, Chichester (2002)
Sharaf, M., Illman, D., Kowalski, B.: Chemometrics. Wiley, New York (1986)
Thurstone, L.: Multiple-factor Analysis: A Development and Expansion of The Vectors of Mind. The university of Chicago committee on publications in biology and medicine. University of Chicago Press, New York (1947)
Thurstone, L., Thurston, T.: Factorial Studies of Intelligence. Psychometrika monograph suplements. The University of Chicago press, Chicago (1941)
Wang, B.B., Mckay, R.I., Abbass, H.A., Barlow, M.: A comparative study for domain ontology guided feature extraction. In: Proceedings of the 26th Australasian Computer Science Conference vol. 16, pp. 69–78. Australian Computer Society, Inc. (2003)
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Wartner, S., Girardi, D., Wiesinger-Widi, M., Trenkler, J., Kleiser, R., Holzinger, A. (2016). Ontology-Guided Principal Component Analysis: Reaching the Limits of the Doctor-in-the-Loop. In: Renda, M., Bursa, M., Holzinger, A., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2016. Lecture Notes in Computer Science(), vol 9832. Springer, Cham. https://doi.org/10.1007/978-3-319-43949-5_2
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