Abstract
This paper describes the steps of building two and efficient monitoring systems in intensive care unit (ICU). We propose two new systems that deal with large data sets and solves the main problems of the current monitoring system. In fact, the current monitoring system in ICU has many issues to detect real states of patients namely critical and normal states. It frequently generates a high number of false alarms having bad effects on the working conditions. Besides, these alarms can threat the patient life by misleading medical staff. Our aim, in this paper, is to avoid false alarms and keep a high level of sensitivity by improving the current monitoring system. In addition, our purpose is to generate groups of patients suffering from similar diseases and building a general model for similar patients. The obtained models will make the classification of new patients possible. To this end, we combine two incremental versions of support vector machines mainly the LASVM and ISVM techniques with the k-prototypes clustering method. The first proposed system is the KP-ISVM which is based on the ISVM technique and the k-prototypes. The second one is called KP-LASVM which takes profits of both the LASVM by reducing the false alarms and the k-prototypes by selecting the appropriate model to start classifying new patients. Both of our proposals are characterized by dealing with large amount of data streams and adding new patients. However, the system using LASVM and k-prototypes i.e. the KP-LASVM has produced the best results compared to the others monitoring systems and based on different evaluation criteria. All experimental results using real-medical databases have been analyzed and have proved the performance of the KP-LASVM.
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References
Baxter, G.D., Monk, A.F., Tan, K., Dear, P.R., Newell, S.J.: Using cognitive task analysis to facilitate the integration of decision support systems into the neonatal intensive care unit. Artificial Intelligence in Medicine 35, 243–257 (2005)
Ben Rejab, F., Nouira, K.: Reducing False Alarms in Intensive Care Units Monitoring System Using Support Vector Machines. CCCM 2010 4, 106–109 (2010)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units. International Journal of Computer Applications, Foundation of Computer Science 49, 41–47 (2012)
Ben Rejab, F., Nouira, K., Trabelsi, A.: On the use of the incremental support vector machines for monitoring systems in intensive care unit. In: TAEECE 2013, pp. 266–270 (2013)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Monitoring Systems in Intensive Care Units using Incremental Support Vector Machines. In: ICDIPC 2013, pp. 273–280 (2013)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Incremental Support Vector Machines for Monitoring Systems in Intensive Care Unit. In: SAI 2013, pp. 496–501 (2013)
Bordes, A., Bottou, L.: The Huller: a simple and efficient online svm. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 505–512. Springer, Heidelberg (2005)
Bordes, A., Ertekin, S., Weston, J., Bottou, J.: Fast Kernel Classifiers With Online And Active Learning. Journal of Machine Learning Research 6, 1579–1619 (2005)
Borowski, M., Siebig, S., Wrede, C., Imhoff, M.: Reducing false alarms of intensive care online monitoring systems: An evaluation of two signal extraction algorithms. In: Computational and Mathematical Methods in Medicine, vol. 2011 (2011)
Cauwenberghs, G., Poggio, T.: Incremental and Decremental Support Vector Machine Learning, pp. 409–415 (2000)
Chambrin, M.C.: Alarms in the intensive care unit: how can the number of false alarms be reduced. Journal of Critical Care 5, 184–188 (2001)
Chang, C., Lin, C.: LIBSVM: A library for support vector machines. Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)
Charbonnie, S., Gentil, S.: A trend-based alarm system to improve patient monitoring in intensive care units. Control Engineering Practice 15, 1039–1050 (2007)
Chen, Y.W., Lin, C.J.: Combining SVMs with various feature selection strategies, http://www.csie.ntu.edu.tw/cjlin/papers/features.pdf
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
Huang, Z.: Clustering large data sets with mixed numeric and categorical values. In: Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 21–34 (1997)
Ivanciuc, O.: Applications of Support Vector Machines in Chemistry. Rev. Comput. Chem. 23, 291–400 (2007)
Luo, J., Pronobis, A., Caputo, B., Jensfelt, P.: Incremental learning for place recognition in dynamic environments. In: Proc. IROS 2007 (2007)
Ming, H.T., Vojislav, K.: Gene extraction for cancer diagnosis by support vector machines an improvement. Artificial Intelligence in Medicine 35, 185–194 (2005)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceeding of the Fifth Berkeley Symposium on Math., Stat. and Prob., pp. 281–296 (1967)
Nouira, K., Trabelsi, A.: Intelligent monitoring system for intensive care units. Journal of Medical Systems 36, 2309–2318 (2011)
Passerini, A., Lippi, M., Frasconi, P.: Predicting Metal-Binding Sites from Protein Sequence. Transactions on Computational Biology and Bioinformatics 9, 203–213 (2012)
Reslan, Z.A.: Clinical alarm management and noise reduction in hospitals. University of Connecticut, Storrs (2007)
Siebig, S., Kuhls, S., Imhoff, M., Langgartner, J., Reng, M., Scholmerich, J., Gather, U., Wrede, C.E.: Collection of annotated data in a clinical validation study for alarm algorithms in intensive care-a methodologic framework. Journal of Critical Care 25, 128–135 (2010)
Tong, T., Koller, D.: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 45–66 (2001)
Tsien, C.: Reducing False Alarms in the Intensive Care Unit: A Systematic Comparison of Four Algorithms. In: Proceedings of the AMIA Annual Fall Symposium. American Medical Informatics Association (1997)
Zhang, S.W., Pan, Q., Zhang, H.C., Zhang, Y.L., Wang, H.Y.: Classification of protein quaternary structure with support vector machine. Bioinformatics 19, 2390–2396 (2003)
Zhang, S.W., Vucetic, S.: Online Training on a Budget of Support Vector Machines Using Twin Prototypes. Statistical Analysis and Data Mining 3, 149–169 (2010)
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Ben Rejab, F., Nouira, K., Trabelsi, A. (2014). Health Monitoring Systems Using Machine Learning Techniques. In: Chen, L., Kapoor, S., Bhatia, R. (eds) Intelligent Systems for Science and Information. Studies in Computational Intelligence, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-04702-7_24
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DOI: https://doi.org/10.1007/978-3-319-04702-7_24
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