Abstract
A concept learning system is expected to be a powerful tool for filtering and analyzing a large amount of data in a variety of scientific fields. A simple application of it to clinical data, however, fails to mine medical information and knowledge. One of the major obstacles in mining a clinical database is time, which is a very important concept in clinical medicine. To be successful in data mining in clinical medicine, an efficient model of clinical data with time and a flexible concept learning system augmented to handle the model are both necessary. Herein we modeled clinical data to easily express and manipulate time and extended a concept learning system to utilize a time-centered clinical data model. The modified concept learning system is based on themessage-value method rather than the traditionalattribute-value method. The object-oriented technology was of great help in modeling time-centered clinical data and in developing a modified concept learning system.
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References
Safran, C., and Chute, C. G., Exploration and exploitation of clinical databases.Int. J. Bio-Med. Comput. 39:151–156, 1995.
Quinlan, J. R., Discovering rules by induction from large collections of examples. In Expert Systems in the Micro Electronic Age (D. Michie, ed.), Edinburgh University Press, Edinburgh, UK, 1979.
Weiss, S. M., and Kapouleas, I., An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, San Mateo, CA, Morgan Kaufmann, pp. 781–787, 1989.
Quinlan, J. R., Comparing connectionist and symbolic learning methods. In (Rivest, G., and Rivest, R., Hanson, S., Drastal, eds.),Computational Learning Theory and Natural Learning Systems: Constraints and Prospects, MIT Press, Cambridge, MA, 1993.
Quinlan, J. R.,C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, 1993.
Budd, T.,An Introduction to Object-Oriented Programming, Addison-Wesley Publishing Company, 1991.
Booch, G.,Object-Oriented Analysis and Design with Applications (second edition), The Benjamin/Cummings Publishing Company, 1994.
Smith, D. N.,IBM Smalltalk the Language, The Benjamin/Cummings Publishing Company, 1995.
Spackman, K. A., Learning categorical criteria in biomedical domains.Proceedings of the Fifth International Machine Learning Conference, Morgan Kaufmann, San Mateo, CA, pp. 36–46, 1988.
Langley, P., Bradshaw, G. L., and Simon, H. A., Rediscovering chemistry with the BACON system. InMachine Learning: An Artificial Intelligence Approach (R. S. Michalski, J. G. Crabinell, and T. M. Mitchell, eds.), Tioga Press, Palo Alto, CA, 1983.
Fisher, D. H., and McKusick, K. B. An empirical comparison of ID3 and back-propagation.Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, pp. 788–793, 1989.
Utgoff, P. E., Incremental induction of decision trees.Machine Learn 4:161–186, 1989.
Hunt, E. B., Martin, J., and Stone, P. J.,Experiments in Induction Academic Press, New York, 1966.
Mingers, J., An empirical comparison of pruning methods for decision-tree induction.Mach. Learn. 4:227–243, 1989.
Brodley, C. E., and Utgoff, P. E., Multivariate versus Univariate Decision Trees, COINS Technical Report 92-8, 1992.
Murthy, S. K., Kasif, S., and Salzberg, S., A System for Induction Oblique Decision Trees,J. Artificial Intell. Res. 2:1–32, 1994.
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Sakamoto, N. Object-oriented development of a concept learning system for time-centered clinical data. J Med Syst 20, 183–196 (1996). https://doi.org/10.1007/BF02263390
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DOI: https://doi.org/10.1007/BF02263390