Abstract
We present a fuzzy clustering approach for detecting flaws in the application of predefined medical protocols. In case of cardiac arrest, the outcome of the intervention is greatly influenced by the precise compliance to certain predefined procedures. We propose a clustering scheme in order to detect possible deviations from the standard protocols. Several interacting agents are employed in this regard. The fuzziness of our approach allows the discovery hybrid data which in this case may be an indication of the medical intervention quality level. We provide experiments on a dataset containing information from real patients that suffered cardiac arrest, on a synthetic dataset and on a standard dataset.
Similar content being viewed by others
References
I. Jacobs et al., Cardiac Arrest and Cardiopulmonary Resuscitation Outcome Reports: Update and Simplification of the Utstein Templates for Resuscitation Registries: A Statement for Healthcare Professionals From a Task Force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of Southern Africa, Circulation 110(21), 3385–3397, 2010
J. Armstrong, Programming Erlang: Software for a Concurrent World (Pragmatic Bookshelf, 2007)
M. Peberdy et al., Cardiopulmonary resuscitation of adults in the hospital: a report of 14720 cardiac arrests from the National Registry of Cardiopulmonary Resuscitation, Resuscitation 58(3), 297–308, 2003
G. Weiss, Multiagent systems: a modern approach to distributed artificial intelligence (MIT Press, Cambridge, MA, USA, 1999)
R.D. Gaceanu, H.F. Pop, An Agent Based Approach for Parallel Constraint Verification, Studia Universitatis Babes-Bolyai Series Informatica, LVIII(3), 5–16, 2013
R.D. Gaceanu, H.F. Pop, A Parallel Clustering Approach for Hybrid Data Discovery, In: V. Novitzká, Štefan Hudák (Eds.), Proceedings of the Twelfth International Conference on Informatics, Informatics 2013 (Slovak Society for Applied Cybernetics and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Spišská Nová Ves, Slovakia, 2013) 240–246
G. Serban, H.F. Pop, Tehnici de Inteligenta Artificiala. Abordari bazate pe Agenti Inteligenti (Ed. Mediamira, Cluj-Napoca, 2004)
G. Serban, Sisteme Mutiagent in inteligenta artificiala distribuita. Arhitecturi si aplicatii (Ed. Risoprint, Cluj-Napoca, 2006)
L.A. Zadeh, Is there a need for Fuzzy Logic, Inf. Sci. 178(13), 2751–2779, 2008
J. Han, M. Kamber, Data Mining: Concepts and Techniques, 3nd ed. (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2011)
L. Chen, X.H. Xu, Y.X. Chen, An Adaptive Ant Colony Clustering Algorithm, Machine Learning and Cybernetics, 2004, Proceedings of 2004 International Conference on 3, 1387–1392, 2004
S. Schockaert, M. De Cock, C. Cornelis, E.E. Kerre, Fuzzy Ant Based Clustering, Ant Colony Optimization and Swarm Intelligence, 11–17, 2004
R.D. Gaceanu, H.F. Pop, An Incremental ASM-based Fuzzy Clustering Algorithm, In: V. Novitzká, Štefan Hudák (Eds.), Informatics’2011, Slovakia, i’11:Proceedings of the Eleventh International Conference on Informatics, Informatics 2011 (Slovak Society for Applied Cybernetics and Informatics, Rožňava, Slovakia, 2011) 198–204
R.D. Gaceanu, H.F. Pop, S.A. Sotoc, An Agent Based Approach for Parallel Constraint Verification LVIII(3), 5–16, 2013
R.S. Parpinelli, H.S Lopes, A.A. Freitas, An Ant Colony Based System for Data Mining: Applications to Medical Data, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001) (Morgan Kaufmann, San Francisco, USA, 2001) 791–797
M. Dorigo, G. Di Caro, The ant colony optimization meta-heuristic, New ideas in optimization (McGraw-Hill Ltd., UK, 1999) 11–32
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Găceanu, R.D., Pop, H.F. Medical procedure breaches detection using a fuzzy clustering approach. centr.eur.j.comp.sci. 4, 127–140 (2014). https://doi.org/10.2478/s13537-014-0219-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.2478/s13537-014-0219-0