Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31903
Case Based Reasoning Technology for Medical Diagnosis

Authors: Abdel-Badeeh M. Salem

Abstract:

Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.

Keywords: Medical Informatics, Computer-Aided MedicalDiagnoses, AI in Medicine, Case-Based Reasoning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074581

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2767

References:


[1] Greer, J. Proceedings of AI-ED 95, World Conference in Artificial Intelligence in Education, Association for Advancement of Computing in Education (AACE), Washington, DC, (1995).
[2] Kolodner, J. Case-Based Reasoning, Morgan Kaufmann, San Mateo, (1993).
[3] Silvana, Q., Pedro, B., and Steen, A. Proceedings of 8th Conference on Artificial Intelligence in Medicine in Europe, AIME, Cascais, Portogal, Springer, (2001).
[4] Hinkle, D. and Toomey, C., Applying Case-Based Reasoning to Manufacturing, AI Magazine, pp. 65-73, (1995).
[5] Rissland, E.L. and Danials, J.J., A Hybrid CBR-IR Approach to Legal Information Retrival, Proceedings of the Fifth International Conference on Artificial Intelligence and Law, (ICAIL-95), pp. 52-61, College Park, MD, (1995).
[6] Salem, A.M. and Baeshen, N., Artificial Intelligence Methodologies for Developing Decision Aiding Systems, Proceedings of Decision Sciences Institute, 5th International Conference, Integrating Technology and Human Decisions: Global Bridges into the 21st Century (D.I.S. 99 Athens), Greece, pp.168-170, (1999).
[7] M. Lenz, S Wess, H Burkhard and B Bartsch, Case based reasoning technology: from foundations to applications, Springer 1998.
[8] B. Heindl. Et al,: A Case-Based Consiliarius for Therapy Recommendation (ICONS) computer-based advise forv calculated antibiotic therapy in intensive care medicine, computer methods and programs in biomedicine 52, pp 117-127, 1997.
[9] Salde, S. Case-Based Reasoning: A Research Paradigm, AI Magazine, Vol. 12, No. 1, 42-55, (1991).
[10] Voss, A. Towards a Methodology for Case Adaptation, Proceedings of the 12th European Conference on Artificial Intelligence, Budapest, Hungary, pp. 147-157, (1996).
[11] Abdel-Badeeh M. Salem , Bassant M. El Bagoury, A Case-Based Adaptation Model for Thyroid Cancer Diagnosis Using Neural Networks, Proceedings of the sixteenth international FLAIRS Conference, AAAI Press, pp.155-159, (2003).
[12] Ian W., Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann, California, (1997).
[13] Salem A.B.M, Roushdy M., and El-Bagoury, B.M., An Expert System for Diagnosis of Cancer Diseases, Proceedings of the 7th International Conference on Soft Computing, MENDEL, pp. 300-305, (2001).
[14] Abdel-Badeeh M. Salem and Rania A. HodHod, A Hybrid Expert System Supporting Diagnosis of Heart Diseases, Proceedings of IFIP 17th World Computer Congress, TC12 Stream on Intelligent Information Processing, Kluwer Academic Publishers, Montreal, Quebec, Canada, pp. 301-305, (2002).