Case Based Reasoning Technology for Medical Diagnosis
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.
[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).
[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).
@article{"International Journal of Medical, Medicine and Health Sciences:59152", author = "Abdel-Badeeh M. Salem", title = "Case Based Reasoning Technology for Medical Diagnosis", 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.", volume = "1", number = "7", pages = "443-5", }
