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The Method for Describing Changes in the Perception of Stenosis in Blood Vessels Caused by an Additional Drug

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
International Workshop on CONCURRENCY, SPECIFICATION, and PROGRAMMING (CS&P 2015), (24; 28-30.09.2015, Rzeszów, Poland).
Języki publikacji
EN
Abstrakty
EN
The decision making depends on the perception of the world and the proper identification of objects. The perception can be modified by various factors, that alter a way of perceiving the object even though the object is not changed (e.g., in the perception of a medical condition, such factors can be drugs or diet). The purpose of this research is to study how the disturbing factors can influence the perception. The idea was to introduce the description of the rules of these changes. We propose a method for evaluating the effect of additional therapy in patients with coronary heart disease based on the tree of the impact. The leaves of the tree provide crossdecision rules of perception changes which could be suggested as a solution to the problem of predicting changes in perception. The problems considered in this paper are associated with the design of classifiers which allow the perception of the object in the context of information related to the decision attribute.
Wydawca
Rocznik
Strony
193--207
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
  • Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
autor
  • Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
  • II Department of Internal Medicine, Jagiellonian University Medical College, Skawinska 8, 31-066 Krakow, Poland
autor
  • II Department of Internal Medicine, Jagiellonian University Medical College, Skawinska 8, 31-066 Krakow, Poland
autor
  • Faculty of Mathematics and Natural Sciences, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
autor
  • Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
autor
  • Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland
Bibliografia
  • [1] Bay S, Pazzani M. Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery, 2001;5(3):213–246. Available from: http://dx.doi.org/10.1023/A:1011429418057, doi:10.1023/A:1011429418057.
  • [2] Bäck M. Leukotriene signaling in atherosclerosis and ischemia. Cardiovacs. Drugs Ther., 2009;23:41–48. doi:10.1007/s10557-008-6140-9.
  • [3] Bazan JG, Osmolski A, Skowron A, Slezak D, Szczuka M, Wroblewski J. Rough set approach to the survival analysis. In: Rough Sets and Current Trends in Computing, Third International Conference, RSCTC 2002, Malvern, PA, USA, October 14-16, 2002, Proceedings. Lecture Notes in Artificial Intelligence 2475, 2002 pp. 522–529. Available from: http://dx.doi.org/10.1007/3-540-45813-1_69, doi:10.1007/3-540-45813-1_69.
  • [4] Bazan JG. Hierarchical classifiers for complex spatio-temporal concepts. In: Transactions on Rough Sets, IX, LNCS 5390, 2008 pp. 474–750. Available from: http://dx.doi.org/10.1007/978-3-540-89876-4_26, doi:10.1007/978-3-540-89876-4_26.
  • [5] Bazan JG, Bazan-Socha S, Buregwa-Czuma S, Pardel PW, Sokolowska B. Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring. In: Federated Conference on Computer Science and Information Systems - FedCSIS 2012, Wroclaw, Poland, 9-12 September 2012, Proceedings. 2012 pp. 279-286. Available from: https://fedcsis.org/proceedings/2012/pliks/227.pdf.
  • [6] Bazan JG, Buregwa-Czuma S, Jankowski A. A Domain Knowledge as A Tool For Improving Classifiers, Fundamenta Informaticae, 2013;127(1-4):495-511. Available from: http://dx.doi.org/10.3233/FI-2013-923, doi:10.3233/FI-2013-923.
  • [7] Bazan JG, Bazan-Socha S, Buregwa-Czuma Dydo L, Rzasa W, Skowron A. A classifier based on a decision tree with verifying cuts, Fundamenta Informaticae, 2016;143(1-2):1–18. Available from: http://dx.doi.org/10.3233/FI-2016-1300, doi:10.3233/FI-2016-1300.
  • [8] Funk CD. Leukotriene modifiers as potential therapeutics for cardiovascular disease. Nat Rev Drug Discov, 2005;4(8):664–672. doi: 10.1038/nrd1796.
  • [9] Bigger JT Jr, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation, 1992;85(1):164–171. Available from: http://dx.doi.org/10.1161/01.CIR.85.1.164.
  • [10] Hansson, GK.: Inflammation, atherosclerosis, and coronary artery disease. The New England Journal of Medicine, 2005;352:1685–1695. doi:10.1056/NEJMra043430.
  • [11] Kralj P, Lavrač N, Gamberger D, Krstačić A. Contrast Set Mining for Distinguishing Between Similar Diseases. In: Artificial Intelligence in Medicine, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, The Netherlands, July 7-11, 2007, Proceedings. Lecture Notes in Computer Science 4594, 2007 pp. 109–118. Available from: http://dx.doi.org/10.1007/978-3-540-73599-1_12, doi:10.1007/978-3-540-73599-1_12.
  • [12] Langohr L, Podpečan V, Petek M, Mozetič I, Gruden K, Lavrač N, Toivonen H. Contrasting Subgroup Discovery. Comput. J., 2013;56(3):289–303. Available from: http://dx.doi.org/10.1093/comjnl/bxs132, doi:10.1093/comjnl/bxs132.
  • [13] Mackay J, Mensah GA. The Atlas of Heart Disease and Stroke. World Health Organization 2004. Available from: http://www.who.int/cardiovascular_diseases/resources/atlas/en/. ISBN: 9241562765, 9789241562768.
  • [14] Malik M. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J., 1996;17:354–381. Available from: http://dx.doi.org/10.1161/01.CIR.93.5.1043.
  • [15] Nguyen HT, Rogers GS. Fundamentals of Mathematical statistics, Volume II, Statistical inference. Springer Verlag, New York 1989. ISBN: 10.0387970207, 13.9780387970202.
  • [16] Pawlak Z, Skowron A. Rudiments of rough sets. Information Sciences, 2007;177(1):3–27. Available from: http://dx.doi.org/10.1016/j.ins.2006.06.003, doi:10.1016/j.ins.2006.06.003.
  • [17] Rautaharju PM, Surawicz B, Gettes LS. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram. Part IV: The ST segment, T and U waves, and the QT interval. Circulation, 2009;119(10):e241–50. Available from: http://dx.doi.org/10.1161/CIRCULATIONAHA.108.191096.
  • [18] Robbins SP, Judge TA. Organizational Behavior, 15th Edition. Prentice Hall, Person, Boston 2012. ISBN: 13.978-0132834872, 10.0132834871.
  • [19] Sanak M, Dropinski J, Sokolowska B, Faber J, Rzeszutko M, Szczeklik A. Pharmacological inhibition of leukotriene biosynthesis: Effects on the heart conductance. Journal of physiology and pharmacology, 2010;61(1):53–58. Available from: http://www.jpp.krakow.pl.
  • [20] Sinai YG. Probability theory. An introductory course. Berlin, Springer-Verlag 1992. doi:10.1007/978-3-662-02845-2.
  • [21] Sneath PHA, Sokal RR. Numerical Taxonomy, San Francisco, W.H. Freeman&Co. 1973. Available from: http://www.jstor.org/stable/2412767,DOI:10.2307/2412767.
  • [22] Sokolowska B. Effect of inhibition of the biosynthesis of leukotrienes on the electrical activity of the heart in stable coronary artery disease. Thesis, Collegium Medicum, Jagiellonian University, Krakow, Poland 2010.
  • [23] Szczeklik A, Nizankowska E, Mastalerz L, Bochenek G. Myocardial ischemia possibly mediated by cysteinyl leukotrienes. J Allergy Clin Immunol, 2002;109(3):572-573. Available from: http://dx.doi.org/10.1067/mai.2002.121700.
  • [24] Zytkow JM, Zembowicz R. Contingency Tables as the Foundation for Concepts, Concept Hierarchies, and Rules: The 49er System Approach. Fundamenta Informatice, 1997;30(3-4):383–399. Available from: http://dl.acm.org/citation.cfm?id=2379371.2379383.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8d894c09-995f-4a1b-b1fd-e64facbd77f4
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