Abdel-Badeeh M. Salem, Vira Shendryk, Sergii Shendryk



Exploiting the Knowledge Computing and Engineering in Medical Informatics and Healthcare

pdf PDF


The field of knowledge computing (KC) has become the most challenging area in the last several years. KC deals with the development of intelligent computing and knowledge-based systems in which knowledge and reasoning play pivotal role. KC consists of three main areas, namely: Document Engineering (DE), Knowledge Engineering (KE), and Reasoning Techniques (RT). KE includes; knowledge acquisition, expert systems, ontologies, knowledge-based systems, knowledge compilation, shells and tools, methodologies, modeling, knowledge management, knowledge discovery, and knowledge representation techniques. The aim of this paper is to make an overview of some of KC techniques and approaches and their applications in medical informatics and healthcare. The paper discusses the following techniques and applications: case-based reasoning approach for cancer and heart diagnosis, ontological engineering for breast cancer knowledge management, and mining patient data using rough sets theory to determine thrombosis disease.


Knowledge Computing, Knowledge Engineering, Knowledge-Based Systems, Ontological Engineering, Knowledge Discovery, Computational Intelligence, Health Informatics


[1] Luger,G.F.,Artificial Intelligence Structure and Strategies for Complex Problem Saving, Addison Wesley, 2005.

[2] Greer, J., Proceedings of AI-ED 95, World Conference on Artificial Intelligence in Education, Association for Advancement of Computing in Education (AACE), 1995.

[3] Waterman D. A., A Guide to Expert Systems, Addison-Wisley, 1986.

[4] Kane, B. and Rucker, D. W., AI in medicine, AI Expert, Kinnucan, 1998.

[5] Salem, A.B. and Katoua, H.S. Web-Based Ontology of Knowledge Engineering, Journal of Communication and Computer, No.9, pp. 516- 522, 2012.

[6] Glushko ,R.J. and Mcgrath, T., Document Engineering, MIT Press, Cambridge, USA, 2005.

[7] Sowa,J.F., Knowledge Representation: Logical Philosophical and Computational Foundations, Brooks Cole Publishing Co., Pacific Grove, CA., 1999.

[8] Michell, T.M., Machine Learning, McGRAWHILL, 1997.

[9] Kolonder, J., Case-Based Reasoning, Morgan Kaufmann, 1993.

[10] Salde, S. Case-Based Reasoning: A Research Paradigm, AI Magazine, Vol. 12, No. 1, pp. 42- 55, 1991.

[11] Abdrabou, E.A. M. and Salem, A.B., Case- Based Reasoning Tools from Shells to Object- Oriented Frameworks. Advanced Studies in Software and Knowledge Engineering, International Book Series "Information Science and Computing", pp. 37-44, 2008.

[12] Salem, A.B., Case Based Reasoning Technology for Medical Diagnosis, Proceedings of World Academy of Science, Engineering and Technology, CESSE, Venice, Italy, Vol. 25, pp. 9-13, 2007.

[13] Salem, A.B. and Voskoglou, M.Gr., Applications of the CBR Methodology to Medicine, Egyptian Computer Science Journal, Vol. 37, No.7, pp. 68-77, 2013.

[14] Pawlak,Z.,Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer, 1991.

[15] Salem, A.B. and Nagaty, K.A., El- Bagoury, B.M., A Hybrid Case-Based Adaptation Model for Thyroid Cancer Diagnosis, Proceedingsof 5th International Conference on Enterprise Information Systems, pp. 58-65, 2003.

[16] Salem A.B.M, Roushdy M., and El- Bagoury, B.M. (2001), An Expert System for Diagnosis of Cancer Diseases, Proceedings of the 7th International Conference on Soft Computing, pp. 300-305, 2001.

[17] Salem, A.B.M., Roushdy, M. and Hod, R.A., A Case Based Expert System for Supporting Diagnosis Of Heart Diseases, International Journal On Artificial Intelligence and Machine Learning, AIML, Tubungen, Germany, Vol. 1, pp.33-39, 2004.

[18] Cios, K. J., Pedrycz, W. and Swiniarski, R. W., Data Mining Methods for Knowledge Discovery, Kluwer, 1998.

[19] Cortes, C. and Vapnik, V., Support vector networks, Machine Learning, Vol. 20, pp. 273- 297, 1995.

[20] Quinlan, J.R, C4.5: Programming for Machine Learning, Morgan Kaufman Publishers, 1993.

[21] Salem,A.B.M. and Mahmoud, S.A.,Mining patient Data Based on Rough Set Theory to Determine Thrombosis Disease, Proceedings of First Intelligence conference on Intelligent Computing and Information Systems,ICICIS, pp. 291-296, 2002.

[22] Bodenreider, O., Burgun, A., Biomedical Ontologies, Medical Informatics: Advances in Knowledge Management and Data Mining in Biomedicine, Springer-Verlag, 2005.

[23] Noy, N.F., McGuinness, D.L., Ontology Development 101: A Guide to Creating Your First Ontology, Stanford Knowledge Systems Laboratory Technical Report, http://protege.stanford.edu/publications/ont ology_development/ontology101-noymcguinness. html

[24] Tankelevciene, L., Damasevicius, R., Characteristics for domain ontologies for web based learning and their application for quality evaluation, Informatics in Education, Vol. 8, pp. 131-152, 2009.

[25] Fernández-López, M. and Gómez-Pérez, A., Deliverable 1.4: A survey on methodologies for developing, maintaining, evaluating and reengineering ontologies. Part of a research project funded by the IST Programme of the Commission of the European Communities as project number IST-2000-29243, 2002.

[26] Salem, A.B.M., Alfonse, M., Ontology versus Semantic Networks for Medical Knowledge Representation, Proceedings of 12th WSEAS CSCC Multiconference (Computers), pp. 769- 774, 2008.

[27]Salem, A.B.M. and Alfonse, M., Ontological Engineering Approach for Breast Cancer Knowledge Management, Proceeding of Med-e- Tel The International eHealth, Telemedicine and Health ICT for Education, Networking and Business, pp. 320-324, 2009.

Cite this paper

Abdel-Badeeh M. Salem, Vira Shendryk, Sergii Shendryk. (2018) Exploiting the Knowledge Computing and Engineering in Medical Informatics and Healthcare. International Journal of Computers, 3, 130-139


Copyright © 2018 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0