Abstract
Efficiency in hospital performance is becoming more and more important. Studies showed that diagnosis can considerably reduce the inefficiency, so one of the most important tasks in achieving greater hospital efficiency is to optimize the diagnostic process. For the best of the patient the diagnostic process has to be optimized regarding the number of the examinations and individualized in order to maximize accuracy, sensitivity and specificity. In addition the duration of the diagnostic process has to be minimized and the process has to be performed on the most reliable equipment. The main contribution of our paper is the introduction of the integrated computerized environment DIAPRO enabling the diagnostic process optimization. The DIAPRO is based on a single approach—evolutionary algorithms.
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REFERENCES
Anderson, H. R., et al., Clinicians Illustrated Dictionary of Cardiology, Science Press, London, 1991.
Baeck, T., Evolutionary Algorithms in Theory and Practice, Oxford University Press, 1996.
Van Bemmel, J. H., and Musen, M. A. (eds.), Handbook of Medical Informatics, Springer-Verlag, 1997.
Burke, E. K., Elliman, D. G., and Weare, R. F., A Genetic Algorithm for University Timetabling, AISB Workshop on Evolutionary Computing, Leeds, (1994).
Burke, E. K., Elliman, D. G., and Weare, R. F., A university timetabling system based on graph coloring and constraint manipulation, J. Res. Comput. Ed. 27(1):1–18 1994.
Burke, E. K., Elliman, D. G., and Weare, R. F., A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems, Proceedings of the 6th International Conference on Genetic Algorithms ICGA'95, Pittsburgh, USA, (Morgan Kaufmann, San Francisco, CA, 1995) 605–610.
Campisi, B., Chicco, D., Vojnovic, D., and Phan-Tan-Luu, R., Experimental design for a pharmaceutical formulation: optimisation and robustness. J. Pharm. Biomed. Anal. 18(1–2):57–65, 1998.
Chen, Y., Narita, M., Tsuji, M., and Sa, S., A genetic algorithm approach to optimization for the radiological worker allocation problem Health Phys. 70(2):180–186, 1996.
Dasgupta, D., and Michalewicz, Z., Evolutionary Algorithms in Engineering Applications, Springer-Verlag, Berlin, Heidelberg, 1997.
Dean, T. L., Firby, R. J., and Miller, D., Hierarchical planning involving deadlines, travel times and resources, Computat. Intel. 4:381–398, 1988.
De Jong, K., Learning with genetic algorithms: An overview, Genetic Algorithms. Bill P. Buckles and Frederick E. Petry, (eds.), IEEE Computer Society Press, Los Alamitos, CA, 1994.
Devereoux, R., Diagnosis and prognosis of mitral valve prolaps.NewEngl. J. Med. 320(16):1077–1079, 1989.
Dorf, R. C., The Engineering Handbook, CRC Press, 1996.
Drabble, B., and Tate, A., The Use of Optimistic and Pessimistic Resource Profiles to Inform Search in an Activity based Planner, 2nd International Conference on AI Planning Systems AIPS-94,1994, pp. 243–248.
El-Kholy, A., and Richards, B., Temporal and Resource Reasoning in Planning the parcPLAN approach, European Conference on AI'96 (1996) 614–618.
Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, AddisonWesley, Reading MA, 1989.
Goldberg, D. E., Genetic and evolutionary algorithms come of age, communication of the ACM. 37(3):113–119, 1994.
Holland, J. H., Adaptation in Natural and Artificial Systems, MIT Press, Cambridge MA, 1975.
Hristov, D. H., and Fallone, B.G., An active set algorithm for treatment planning optimization, Med Phys. 24(9):1455–1464, 1997.
Kokol, P., and Kunej, A., EOP-The Robust User Oriented Paradigm for Designing Engineering Software Systems, Proceedings of DPIC, John Wiley, Chichester, 1991.
Kokol, P., et al., Decision trees and automatic learning and their use in cardiology. J. Med. Systems 19(4), 1994.
Kokol, P., et al., Diagnostic Process Optimisation: A Two Levelled Approach, Proceedings of CBMS 95, IEEE Computer Society Press, 1995.
Kokol, P., Stiglic, B., and Zumer, V., Metaparadigm: a soft and situation oriented MIS design approach. Int. J. Bio-Med. Comput. 39:243–256, 1995.
Koza, J. R., Genetic Programming: On the Programming of Computers by Natural Selection, MIT Press, 1992.
Kumar, A. D., Kumar, A. R., Kekre, S., Prietula, M. J., and Ow, P. S., Multi-agent Systems and Organizational Structure: The Support of Hospital Patient Scheduling, 3rd International Conference on Expert Systems and the Leading Edge in Production and Operations Management (1989) 551–566.
Kumar, A.D., Ow, P. S., and Prietula, M. J., Organizational simulation and information systems design: An operations level example, management science 39(2):218–240, 1993
Laakso, K., Simola, K., and Holmberg, B., Examples of Reliability Assessment of Maintenance in Finish Nuclear Power Plants. Proceedings of IAEA Meeting, IAEA, Stockholm (1990).
Laborie, P., and Ghallab, M., Planning with Sharable Resource Constraints, 14th International Joint Conference on AI-IJCAI'95 (1995) 1643–1649.
Lof, J., Lind, B. K., and Brahme, A., An adaptive control algorithm for optimization of intensity modulated radiotherapy considering uncertainties in beam profiles, patient set-up and internal organ motion. Phys. Med. Biol. 43(6):1605–1628, 1998.
Malcic, I., and Ivancevic, D., Databases and decision system for diagnosis of congenital heart disease. Databases for Cardiology: G. T., Meester, and F., Pinciroli, (eds.), Kluwer Academic Publisher, Dordrectht, pp. 273–288, 1991.
Markiewicz, W., et al, Mitral valve Prolaps in one hundred presumably young females. Circulation 53(3):464–473, 1976.
Podgorelec, V., and Kokol, P., Genetic algorithm based system for patient scheduling in highly constrained situations, J. Med. Syst. 21(6):417–427, 1997.
Quinlan, J. R., Simplifying decision trees. Int. J. Man-Machine Studies 27:221–234, 1987.
Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
Rosko, M.D., and Chilingerian, J.A., Estimating hospital inefficiency: Does case mix matter?, J. Med. Syst. 23(1):83–97, 1999.
Smith, S. F., Integrating Planning and Scheduling: Towards Effective Coordination in Complex, Resource-Constraint Domains, Italian Planning Workshop, Keynote Address, 1993.
Spyropoulos, C. D., Kokkotos, S., and Marinagi, C., Planning and Scheduling Patient Tests, Artificial Intelligence in Medicine, Lecture Notes in Computer Science. vol. 1211, Springer, pp. 307–318, 1997.
Takayama, K., Fujikawa, M. T., and Nagai, T., Artificial neural network as a novel method to optimize pharmaceutical formulations, Pharm Res. 16(1):1–6, 1999.
Thompson, J. M., and Dowsland, K.A., General cooling schedules for a simulated annealing based timetabling system. The Practice and Theory of Automated Timetabling, Lecture Notes in Computer Science 1153:345–364, 1996.
Wang, B. B., Ozcan, Y. A., Wan, T. T. H., and Harrison, J., Trends in hospital efficiency among metropolitan markets. J. Med. Syst. 23(2):83–97, 1999.
Wilkins, D. E., Can AI planners solve practical problems. Computat. Intell. 6:232–246, 1990.
Yu, Y., Schell, M. C., and Zhang, J. B., Decision theoretic steering and genetic algorithm optimization: application to stereotactic radiosurgery treatment planning. Med. Phys. 24(11):1742–1750, 1997.
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Podgorelec, V., Kokol, P. Towards More Optimal Medical Diagnosing with Evolutionary Algorithms. Journal of Medical Systems 25, 195–219 (2001). https://doi.org/10.1023/A:1010733016906
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DOI: https://doi.org/10.1023/A:1010733016906