Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

Aankita Kaur, Mansaf Alam

Department of Computer science, Jamia Millia Islamia University, New Delhi, India.
Department of Mathematics, Jamia Millia Islamia University, New Delhi, India.

DOI: 10.4236/ajibm.2013.31005                

Abstract

In this paper, we are describing role of knowledge engineering in the development of a hybrid knowledge based medical information system. Knowledge engineering plays important role in development of various technologies such as: expert systems, neural network, artificial intelligence, hybrid intelligent systems, data mining, decision support systems, and knowledge based systems etc. The hybrid medical information system mainly consists of medical information system and medical knowledge base systems. These poly techniques of knowledge engineering when integrated with hybrid techniques of intelligent systems for designing, implementing knowledge bases to deal with medical informational data of Atrial Fibrillation. Atrial Fibrillation is the most common heart rhythm disorder which increases the risk of mortality and morbidity.

Keywords

Artificial Intelligence (AI); Decision Making; Knowledge Engineering; Knowledge Base; Mortality; Morbidity; Knowledge Management (KM); Experience Management (EM); Knowledge Base System (KBS); Knowledge Management System (KMS); Atrial Fibrillation (AFib); Knowledge Discovery Techniques (KDT); Medical Information System; Medical Information Based Decision Making; Hybrid Intelligent Medical Information System (HIMIS); Hybrid Knowledge Based System

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Z. Sun and G. Finnie, “Intelligent Techniques in E-Commerce: A Case-Based Reasoning Perspective,” Springer-Verlag, Heidelberg, 2004.
[2]	R. Bergmann, “Experience Management: Foundations, Development Methodology and Internet-Based Applications,” Springer, Berlin, 2002.
[3]	Z. Sun, “A Waterfall Model for Knowledge Management and Experience Management,” Proceedings of International Conference on Hybrid Intelligent Systems, Kitakyushu, 6-8 December 2004, pp. 472-475.
[4]	H. Hasan and M. Handzic, “Australian Studies in Knowledge Management,” University of Wollongong Press, Wollongong, 2003.
[5]	E. J. Benjamin, P. A. Wolf, R. B. D. Agostino, et al., “Impact of Atrial Fibrillation on the Risk of Death: The Framingham Heart Study,” Circulation, Vol. 98, No. 10, 1998, pp. 946-952.
[6]	K. Lakshminarayan, C. A. Solid, A. J. Colins, et al., “Atrial Fibrillation and Stroke in the General Medicare Population—A 10-Year Perspective (1992 to 2002),” Stroke, Vol. 37, No. 8, 2006, pp. 1969-1974. doi:10.1161/01.STR.0000230607.07928.17
[7]	P. A. Wolf, R. D. Abbott and W. B. Kanne, “Atrial Fibrillation as an Independent Risk Factor for Stroke: The Framingham Heart Study,” Stroke, Vol. 22, 1991, pp. 983-988. doi:10.1161/01.STR.22.8.983
[8]	T. Thom, N. Haase, W. Rosamond, et al., “Heart Disease and Stroke Statistics—A Report from American Heart Association Statistics Committee and Stroke Statistics Subcommittee,” Circulation, Vol. 113, 2006, p. e85.
[9]	K. E. Burn-Thornton, “Making the Most of Data in Order to Provide Accurate Clinical Decision Support Systems for the Use in the Determination of Heart Disease,” Proceeding of Third International Conference on Data Mining, Bologna, 25-27 September 2002, pp. 839-848.
[10]	I. Watson, “Applying Case-Based Reasoning: Techniques for Enterprise Systems,” Morgan Kaufmann Publishers, San Francisco, 1997.
[11]	J. Surma and K. Vanhoff, “Integrating Rules and Cases for ICCBR,” Lecture Notes in Artificial Intelligence 1010, Sesimbra, 1995.
[12]	S. Montani, R. Bellazzi, L. Portinale, S. Fiocchi and M. A. Stefanelli, “Case-Based Retrieval System for Diabetic Patients Therapy,” Proceedings of IDAMAP 98 Working Notes, Workshop ECAI 98, Brighton, 1998, pp. 64-70.
[13]	Z. Sun and G. Finnie, “Experience Based Reasoning for Recognizing Fraud and Deception,” Proceedings of International Conference on Hybrid Intelligent Systems (HIIS 2004), Kitakyushu, 6-8 December 2004, pp. 80-85.
[14]	A. Kaur, “Hybrid Approach: Predictive Data Mining Model for Atrial Fibrillatiom,” International Conference on World Congress on Information and Communication Technologies, Mumbai, 11-14 December 2012, pp. 126-131. doi:10.1109/WICT.2011.6141230s
[15]	S. L. Kendal, K. Ashton and X. Chen, “A Brief Overview of HyM: A Methodology for the Development of Hybrid Intelligent Information Systems,” Proceedings of the 15th International Conference on Software Engineering and Knowledge Engineering, San Francisco, 1-3 July 2003, pp. 322-326.
[16]	U. Rashad, P. Arullendran, M. Hawthorne and S. Kendal, “A Hybrid Medical Information System for Diagnosis of Dizziness,” Proceedings of the 4th International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, Milos Island, 20-22 June 2001.
[17]	S. L. Kendal, K. Ashton and X. Chen, “A Brief Review of HyM: A Methodology for the Development of Hybrid Intelligent Information Systems,” Proceedings of the 15th International Conference on Software Engineering and Knowledge Engineering, San Francisco, 1-3 July 2003, ISBN: 1-891706-12-8.
[18]	S. L. Kendal, X. Chen and A. Masters, “HyM: A Methodology for Development of Integrated Hybrid Intelligent Information Systems,” Proceedings of Fusion 2000—3rd International Conference on Information Fusion, Paris, 10-13 July 2000, pp. WEC4/17-WEC4/22.
[19]	G. G. Towell, J. W. Shavlik and M. O. Noordwier, “Refinement of Approximate Domain Theories by Knowledge Based-Artificial Neural Network,” Proceedings of the 8th National Conference on Artificial Intelligence, Boston, July 1990, pp. 861-866.
[20]	A. B. Tickle, R. Andrews, M. Goela and J. Diederich, “The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded within Trained Artificial Neural Networks,” IEEE Transactions on Neural Networks, Vol. 9, No. 6, 1998, pp. 1057-1068.
[21]	L. M. Fu, “Knowledge Based Connectionism for Revising Domain Theories,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 1, 1993, pp. 173-182. doi:10.1109/21.214775
[22]	M. G. Tsipouras, D. I. Fotiadis and D. Sideris, “An Arrhythmia Classification System Based on the RR-Interval Signal,” Artificial Intelligence in Medicine, Vol. 33, No. 3, 2005, pp. 237-250. doi:10.1016/j.artmed.2004.03.007
[23]	B. M. Asl, S. K. Setarehdan and M. Mohebbi, “Support Vector Machine-Based Arrhythmia Classification Using Reduced Features of Heart Rate Variability Signal,” Artificial Intelligence in Medicine, Vol. 44, No. 1, 2008, pp. 51-64. doi:10.1016/j.artmed.2008.04.007