Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(870.96KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their Symptoms and Signs
Prempal Singh Tomar, Ranjit Singh, P K Saxena, Jeetu Sharma
Pages - 216 - 224     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
MORE INFORMATION
KEYWORDS
Clinical Support System, Artificial Intelligence, Case-Based Reasoning, Pathologist
ABSTRACT
The clinical decision support system using the case based reasoning (CBR) methodology of Artificial Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided 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. In this paper, we have used the Case Based Reasoning methodology to develop a clinical decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients. Statistical analysis has been done to determine the importance values of the case features. The retrieval strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively. Thus, the result showed that the system is capable of assisting an inexperience pathologist in making accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-assessment, and quality control. In this paper, clinical decision support system prototype is developed for supporting diagnosis of occupational lung diseases from their symptoms and signs through employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with the advantage of Object Oriented Programming technology
CITED BY (9)  
1 kiruba, h. r., & arasu, g. t. (2014). an intelligent--agent based framework for liver disorder diagnosis using artificial intelligence techniques. journal of theoretical & applied information technology, 69(1).
2 Sutanto, D. H., Herman, N. S., & Ghani, M. K. A. (2014). Trend of Case Based Reasoning for Chronic Disease Diagnosis: A Review.
3 Sutanto, D. H., Herman, N. S., Ghani, M., & Abd, K. (2014). Trend of Case Based Reasoning in Diagnosing Chronic Disease: A Review. Advanced Science Letters, 20(10-12), 1740-1744.
4 Demigha, S., & Balleyguier, C. (2013, January). Teaching Cases for Capturing, Capitalizing and Re-Using Knowledge: A Case Study in Senology. In Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning, ICICKM13, the George Washington University (Vol. 2, pp. 104-113).
5 Tomar, P. P., Singh, R., & Saxena, P. K. (2012). A Medical Multimedia based Clinical Decision Support System for Operational Chronic Lung Diseases Diagnosis and Training. International Journal of Computer Applications, 49(8), 1-12.
6 Prem Pal Singh Tomar, Ranjit Singh, P K Saxena, B K Sharma, “A medical multimedia based DSS for heart diseases diagnosis and training”, Canadian Journal on Biomedical Engineering & Technology Vol. 3 No. 2, pp. 30-38, February 2012.
7 Tomar, P. P. S., Singh, R., & Saxena, P. K. Multimedia Based Clinical Decision Support System for heart diseases diagnosis using rule based technique.
8 Kiruba, H. R., & Arasu, G. T. Evaluation of Liver Disorder Classifiers Using Agent Based Artificial Intelligence Techniques.
9 Pal, P., Singh Tomar, R. S., & Saxena, P. K. (2011). Multimedia Based MDSS For Chronic Lung Diseases Diagnosis Using Rule Based Technique.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 iSEEK 
6 Bielefeld Academic Search Engine (BASE) 
7 Scribd 
8 SlideShare 
9 PdfSR 
1 R. Schank and R. Abelson (eds.), Scripts, Plans, Goals and Understanding, Lawrence Erlbaum Associates, Hillsdale, NJ, 1977
2 R. Schank, Dynamic Memory: A Theory of Reminding and Learning in Computers and People, Cambridge University Press, New York, 1982
3 J. L. Kolodner, Maintaining organization in a dynamic long term memory, Cognitive Science, vol. 7, pp. 243–280, 1983.
4 J. L. Kolodner, Reconstructive memory, a computermodel, Cognitive Science, vol. 7,pp. 281–328, 1983.
5 R. L. Simpson, A computer model of case-based reasoning in problem solving: an investigation in the domain of dispute mediation, Ph.D. dissertation, School of Information and Computer Science, Georgia Institute of Technology, Atlanta, GA, 1985.
6 K. Sycara, Using case-based reasoning for plan adaptation and repair, in Proceedings of the Case-Based Reasoning Workshop, DARPA, Clearwater Beach, FL, Morgan Kaufmann,San Francisco, pp. 425–434, 1988.
7 K. J. Hammond, Case-Based Planning, Academic Press, San Diego, CA, 1989.
8 T. R. Hinrihs, Problem Solving in OpenWorlds, Lawrence Erlbaum Associates, Hillsdale,NJ, 1992.
9 R. Bareiss, Exemplar-Based Knowledge Acquisition: A Unified Approach to ConceptRepresentation, Classification, and Learning, Academic Press, San Diego, CA, 1989.
10 J. L. Kolodner (Ed.),Case-Based Reasoning,Morgan Kaufmann Publishers: California, 1993
11 M. Lenz, S. Wess, H. Burkhard and B. Bartsch, Case-Based Reasoning Technology:From Foundations to applications, Springer, 1998.
12 B. Heindl. Et al,: A Case-Based Consiliarius for TherapyRecommendation (ICONS): Computer-Based Advice for calculated Antibiotic Therapy in Intensive Care Medicine. Computer Methods and Programms in Biomedicine 52 , pp. 117-127, 1997.
13 E. L. Rissland, J. Kolodner, & D. Waltz, Case-based reasoning from DARBA: “Machine learning program plan”. Proceedings of the case-Based ReasoningWorkshop. San Mateo, CA: Morgan Kaufmann, pp. 1-13,1998.
14 Fieschi M, Dufour J-C, Staccini P, et al. Medical decision support systems: old dilemmas and new paradigms? Tracks for successful integration and implementation. Methods Inform Med 2003; 42:190–198.8
15 Decision support systems for antibiotic prescribing, Vitali Sintchenkoa,b,c, Enrico Coierac and Gwendolyn L. Gilberta,b , Current Opinion in Infectious Diseases 2008,21:573–579
16 Medical applications in case based reasoning , ALEC HOLT, ISABELLE BICHINDARITZ, RAINER SCHMIDT, and PETRA PERNER , The Knowledge Enineering review, Vol.20:3, 289-292 – 2006, Cambridge University Press
17 Graham B, Detsky AS, the application of decision analysis to the surgical treatment of early osteoarthritis of wrist. J Bone Joint Surg Br 2001:83:650-4
18 Detsky AS, Nalie G, Krahn MD, Naimark D, Redelmeier DA. Primer on medical decision analysis : Part 1- Getting started. Med Decision Making 1997 : 17:123-5
Mr. Prempal Singh Tomar
DEI, Agra - India
singhppst@rediffmail.com
Mr. Ranjit Singh
- India
Mr. P K Saxena
- India
Dr. Jeetu Sharma
- India