[1] R. a. S. Perrin, N., "RFID and Bar Codes Critical Importance in Enhancing Safe Patient Care,"Journal of Health care Information Management,vol.18(4), pp. 33-39, 2004.
[2] R. Kaushal and D. W. Bates, "Information technology and medication safety:what is the benefit?,"Quality and Safety in Health Care,vol. 11, pp. 261-265,September 1, 2002 2002.
[3] E. Oren, et al., "Impact of emerging technologies on medication errors and adverse drug events,"American Journal of Health-System Pharmacy,vol. 60,pp. 1447-1458, July 1, 2003 2003.
[4] L. E. MOODY, et al., "Electronic Health Records Documentation in Nursing:Nurses' Perceptions, Attitudes, and Preferences,"Computers Informatics Nursing,vol. 22, pp. 337-344, 2004.
[5] P. B. Jon Timmis, Emma Hart, "Artificial immune systems "second international conference, ICARIS, Edinburgh,2003.
[6] L. R. d. Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Paradigm: Springer-Verlag New York, Inc., 2002.
[7] J. Timmis, Neal, M., Hunt, J. , "An Artificial Immune System for Data Analysis,"Biosystems,vol. 55, pp. 143-150, 2000.
[8] K. Finkenzeller, inRFID Handbook, ed: John Wiley & Sons, Ltd, 2003.
[9] A. Lahtela, "A Short Overview of the RFID Technology in Healthcare,"presented at the Proceedings of the 2009 Fourth International Conference on Systems and Networks Communications, 2009.
[10] J. Timmis, et al., "Immune Systems and Computation: An Interdisciplinary Adventure," in Unconventional Computing. vol. 5204, C. Calude, et al., Eds.,ed: Springer Berlin Heidelberg, 2008, pp. 8-18.
[11] R. L. King, et al., "The Biological Basis of the Immune System as a Model for Intelligent Agents," presented at the Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International ParallelProcessing Symposium and 10th Symposium on Parallel and Distributed Processing, 1999.
[12] H. Lu, "Artificial Immune System for Anomaly Detection," in Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on, 2008, pp. 340-343.
[13] N. Lay and I. Bate, "Applying artificial immune systems to real-time embedded systems," in Evolutionary Computation, 2007. CEC 2007. IEEECongress on, 2007, pp. 3743-3750.
[14] L. N. d. Castro and F. J. V. Zuben, "The Clonal Selection Algorithm withEngineering Applications 1," presented at the Genetic and EvolutionaryComputation Conference, 2000.
[15] F. Burnet, "A modification of Jerne'stheory of antibody production using theconcept of clonal selection,"CA: A Cancer Journal for Clinicians,vol. 26, pp.119-21, 1976.
[16] K. Igawa and H. Ohashi, "A negative selection algorithm for classification andreduction of the noise effect,"Appl.Soft Comput.,vol. 9, pp. 431-438, 2009.
[17] H. Wang, et al., "PSO-Optimized Negative Selection Algorithm for AnomalyDetection," inApplications of Soft Computing. vol. 52, E. Avineri, et al., Eds.,ed: Springer Berlin Heidelberg, 2009, pp. 13-21.
[18] F. González, et al., "A Randomized Real-Valued Negative SelectionAlgorithm," inArtificial Immune Systems. vol. 2787, J. Timmis, et al., Eds.,ed: Springer Berlin Heidelberg, 2003, pp. 261-272.
[19] J. Hunt, et al., "Jisys: Development of an artificial immune system for realworld applications," inArtificial Immune Systems and their Applications, ed:Springer, 1998, pp. 157-186.
[20] R. C. Wasielewski, "Wireless technologies for the orthopaedics: Diagnosticsand surgical applications," in Biomedical Wireless Technologies, Networks,and Sensing Systems (BioWireleSS), 2011 IEEE Topical Conference on, 2011,pp. 1-2.
[21] M. Cai, "A Novel Immunity-Based Model for Anomaly Detection," presented at the Proceedings of the 2008 International Conference on Computer Scienceand Software Engineering-Volume 03, 2008.
[22] M. C. Varela, M.; Chana, M.; Gomez-Mestre, I.; Asensio, R.; Galdos, P.,"Clinical implications of temperature curve complexity in critically illpatients,"Crit. Care Med,vol. 12, pp. 2764-2771, 2005.
[23] C. Yu, et al., "Design of Clinic Patient Body Temperature Wireless RemoteConcentration Monitor System Based on VI," inEducation Technology andComputer Science, 2009. ETCS '09. First International Workshopon, 2009,pp. 630-634.
[24] T. Landolsi, et al., "Wireless stand-alone portable patient monitoring and logging system,"Journal of Communications, vol. 2, pp. 65-70, 2007.
[25] S. Stolfo, et al., "Anomaly Detection in Computer Security and an Application to File System Accesses," in Foundations of Intelligent Systems. vol. 3488,M.-S. Hacid, et al., Eds., ed: Springer Berlin Heidelberg, 2005, pp. 14-28.
[26] F. Gonzalez, et al., "Combining negative selection and classification techniques for anomaly detection," in Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, 2002, pp. 705-710.
[27] M. Gong, et al., "Short Communication: An efficient negative selection algorithm with further training for anomaly detection,"Know.-Based Syst.,vol. 30, pp. 185-191, 2012.
[28] J. Sifakis, et al., "Building models of real-time systems from application software,"Proceedings of the IEEE,vol. 91, pp. 100-111, 2003.
[29] J. Timmis and M.Neal, "A resource limited artificial immune system for data analysis,"Knowledge-Based Systems,vol. 14, pp. 121-130, 2001.
[2] R. Kaushal and D. W. Bates, "Information technology and medication safety:what is the benefit?,"Quality and Safety in Health Care,vol. 11, pp. 261-265,September 1, 2002 2002.
[3] E. Oren, et al., "Impact of emerging technologies on medication errors and adverse drug events,"American Journal of Health-System Pharmacy,vol. 60,pp. 1447-1458, July 1, 2003 2003.
[4] L. E. MOODY, et al., "Electronic Health Records Documentation in Nursing:Nurses' Perceptions, Attitudes, and Preferences,"Computers Informatics Nursing,vol. 22, pp. 337-344, 2004.
[5] P. B. Jon Timmis, Emma Hart, "Artificial immune systems "second international conference, ICARIS, Edinburgh,2003.
[6] L. R. d. Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Paradigm: Springer-Verlag New York, Inc., 2002.
[7] J. Timmis, Neal, M., Hunt, J. , "An Artificial Immune System for Data Analysis,"Biosystems,vol. 55, pp. 143-150, 2000.
[8] K. Finkenzeller, inRFID Handbook, ed: John Wiley & Sons, Ltd, 2003.
[9] A. Lahtela, "A Short Overview of the RFID Technology in Healthcare,"presented at the Proceedings of the 2009 Fourth International Conference on Systems and Networks Communications, 2009.
[10] J. Timmis, et al., "Immune Systems and Computation: An Interdisciplinary Adventure," in Unconventional Computing. vol. 5204, C. Calude, et al., Eds.,ed: Springer Berlin Heidelberg, 2008, pp. 8-18.
[11] R. L. King, et al., "The Biological Basis of the Immune System as a Model for Intelligent Agents," presented at the Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International ParallelProcessing Symposium and 10th Symposium on Parallel and Distributed Processing, 1999.
[12] H. Lu, "Artificial Immune System for Anomaly Detection," in Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on, 2008, pp. 340-343.
[13] N. Lay and I. Bate, "Applying artificial immune systems to real-time embedded systems," in Evolutionary Computation, 2007. CEC 2007. IEEECongress on, 2007, pp. 3743-3750.
[14] L. N. d. Castro and F. J. V. Zuben, "The Clonal Selection Algorithm withEngineering Applications 1," presented at the Genetic and EvolutionaryComputation Conference, 2000.
[15] F. Burnet, "A modification of Jerne'stheory of antibody production using theconcept of clonal selection,"CA: A Cancer Journal for Clinicians,vol. 26, pp.119-21, 1976.
[16] K. Igawa and H. Ohashi, "A negative selection algorithm for classification andreduction of the noise effect,"Appl.Soft Comput.,vol. 9, pp. 431-438, 2009.
[17] H. Wang, et al., "PSO-Optimized Negative Selection Algorithm for AnomalyDetection," inApplications of Soft Computing. vol. 52, E. Avineri, et al., Eds.,ed: Springer Berlin Heidelberg, 2009, pp. 13-21.
[18] F. González, et al., "A Randomized Real-Valued Negative SelectionAlgorithm," inArtificial Immune Systems. vol. 2787, J. Timmis, et al., Eds.,ed: Springer Berlin Heidelberg, 2003, pp. 261-272.
[19] J. Hunt, et al., "Jisys: Development of an artificial immune system for realworld applications," inArtificial Immune Systems and their Applications, ed:Springer, 1998, pp. 157-186.
[20] R. C. Wasielewski, "Wireless technologies for the orthopaedics: Diagnosticsand surgical applications," in Biomedical Wireless Technologies, Networks,and Sensing Systems (BioWireleSS), 2011 IEEE Topical Conference on, 2011,pp. 1-2.
[21] M. Cai, "A Novel Immunity-Based Model for Anomaly Detection," presented at the Proceedings of the 2008 International Conference on Computer Scienceand Software Engineering-Volume 03, 2008.
[22] M. C. Varela, M.; Chana, M.; Gomez-Mestre, I.; Asensio, R.; Galdos, P.,"Clinical implications of temperature curve complexity in critically illpatients,"Crit. Care Med,vol. 12, pp. 2764-2771, 2005.
[23] C. Yu, et al., "Design of Clinic Patient Body Temperature Wireless RemoteConcentration Monitor System Based on VI," inEducation Technology andComputer Science, 2009. ETCS '09. First International Workshopon, 2009,pp. 630-634.
[24] T. Landolsi, et al., "Wireless stand-alone portable patient monitoring and logging system,"Journal of Communications, vol. 2, pp. 65-70, 2007.
[25] S. Stolfo, et al., "Anomaly Detection in Computer Security and an Application to File System Accesses," in Foundations of Intelligent Systems. vol. 3488,M.-S. Hacid, et al., Eds., ed: Springer Berlin Heidelberg, 2005, pp. 14-28.
[26] F. Gonzalez, et al., "Combining negative selection and classification techniques for anomaly detection," in Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, 2002, pp. 705-710.
[27] M. Gong, et al., "Short Communication: An efficient negative selection algorithm with further training for anomaly detection,"Know.-Based Syst.,vol. 30, pp. 185-191, 2012.
[28] J. Sifakis, et al., "Building models of real-time systems from application software,"Proceedings of the IEEE,vol. 91, pp. 100-111, 2003.
[29] J. Timmis and M.Neal, "A resource limited artificial immune system for data analysis,"Knowledge-Based Systems,vol. 14, pp. 121-130, 2001.
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Affiliations
Sri Listia Rosa
Soft Computing Research Group,
Faculty of Computer Science and Information System
Universiti Teknologi Malaysia, 81310,
Johor, Malaysia
Siti Mariyam Shamsuddin
Soft Computing Research Group,
Faculty of Computer Science and Information System
Universiti Teknologi Malaysia, 81310,
Johor, Malaysia
Evizal Evizal
Wireless Communication Centre,
Faculty of Electrical Engineering
Universiti Teknologi Malaysia, 81310,
Johor, Malaysia
An Immune Based Patient Anomaly Detection using RFID Technology
Abstract
Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI:Â 10.18495/comengapp.21.121142