Abstract
Clinical examples are widely used as learning and testing sets for newly proposed artificial intelligence-based classifiers of signals and images in medicine. The results obtained from testing are usually taken as an estimate of the behavior of automatic recognition system in presence of unknown input in the future. This paper investigates and discusses the consequences of the non-uniform representation of the medical knowledge in such clinically-derived experimental sets. Additional challenges come from the nonlinear representation of the patient status in particular parameters’ domain and from the uncertainty of the reference provided usually by human experts. The presented solution consists of representation of all available cases in multidimensional diagnostic parameters or patient status spaces. This provides the option for independent linearization of selected dimensions. The recruitment to the learning set is then based on the case-to-case distance as selection criterion. In result, the classifier may be trained and tested in a more suitable way to cope with unpredicted patterns.
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References
Aldroubi, A., Feichtinger, H.: Exact Iterative Reconstruction Algorithm for Multivariate Irregularly Sampled Functions in Spline-like Spaces: the L p Theory. Proc. Amer. Math. Soc. 126(9), 2677–2686 (1998)
Augustyniak, P.: Automatic Understanding of ECG Signal. In: Kopotek, A., Wierzchon, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining, pp. 591–597. Springer, Heidelberg (2005)
Haussler, D.: Quantifying Inductive Bias: AI Learning Algorithms and Valiant’s Learning Framework. Artificial Intelligence 36, 177–221 (1988)
Moody, G.B., Mark, R.G.: The MIT-BIH Arrhythmia Database on CD-ROM and Software for Use with it. In: Computers in Cardiology 1990, pp. 185–188 (1990)
Osowski, S.: Neural Networks for Information Processing. WUT Publishing House, Warsaw (2000) (in Polish)
Rutkowski, L., Tadeusiewicz, R. (eds.): Neural Networks and Soft Computing. Polish Neural Network Society (2000)
Stanisz, A.: Accessible Course of the Statistics with STATISTICA PL and Examples from Medicine. StatSoft Poland, Krakow (2006) (in Polish)
Straszecka, E., Straszecka, J.: Distance Based Classifiers and their Use to Analysis of Data Concerned Acute Coronary Syndromes. Image Processing & Communications 9(3-4), 53–69 (2003)
Straszecka, E., Straszecka, J.: Interpretation of Medical Symptoms Using Fuzzy Focal Element. In: Kurzynski, M., et al. (eds.) Computer Recognition Systems. Springer, Heidelberg (2005)
Tadeusiewicz, R.: Neural Networks. RM Academic Publishing House, Warsaw (1993) (in Polish)
Tadeusiewicz, R., Augustyniak, P.: Information Flow and Data Reduction in the ECG Interpretation Process. In: IEEE 27 Annual EMBS Conf., paper 88 (2005)
Tadeusiewicz, R., Ogiela, L.: Selected cognitive categorization systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 1127–1136. Springer, Heidelberg (2008)
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Augustyniak, P. (2010). Clinical Examples as Non-uniform Learning and Testing Sets. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_72
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DOI: https://doi.org/10.1007/978-3-642-13208-7_72
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