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Machine Learning for Data Mining in Medicine

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Artificial Intelligence in Medicine (AIMDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1620))

Abstract

Large collections of medical data are a valuable resource from which potentially new and useful knowledge can be discovered through data mining. This paper gives an overview of machine learn- ing approaches used in mining of medical data, distinguishing between symbolic and sub-symbolic data mining methods, and giving references to applications of these methods in medicine. In addition, the paper presents selected measures for performance evaluation used in medical prediction and classification problems, proposing also some alternative measures for rule evaluation that can be used in ranking and filtering of induced rule sets.

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References

  1. Aamodt, A. and Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches, AI Communications, 7(1) 39–59 (1994).

    Google Scholar 

  2. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A.I.: Fast discovery of association rules. In U.M. Fayyad, G. Piatetski-Shapiro, P. Smyth and R. Uthurusamy (Eds.) Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996, pp. 307–328.

    Google Scholar 

  3. Aha, D., Kibler, D. and Albert, M.: Instance-based learning algorithms, Machine Learning, 6: 37–66 (1991).

    Google Scholar 

  4. Astion, M.L. and Wielding, P.: The application of backpropagation neural networks to problems in pathology and laboratory medicine, Arch Pathol Lab Med, 116: 995–1001 (1992).

    Google Scholar 

  5. Baxt, W.G.: Application of artificial neural networks to clinical medicine, Lancet, 364(8983) 1135–1138 (1995).

    Article  Google Scholar 

  6. Bradburn, C., Zeleznikow, J. and Adams, A.: Florence: synthesis of case-based and model-based reasoning in a nursing care planning system, Computers in Nursing, 11(1): 20–24 (1993).

    Google Scholar 

  7. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont, 1984.

    MATH  Google Scholar 

  8. Brossette, S.E., Sprague, A.P., Hardin, J.M., Waites, K.B., Jones, W.T. and Moser, S.A.: Association rules and data mining in hospital infection control and public health surveillance. Journal of the American Medical Inform. Assoc. 5(4): 373–81 (1998).

    Google Scholar 

  9. Carpenter, G.A. and Tan, A.H.: Rule extraction, fuzzy ARTMAP and medical databases. In: Proc. World Cong. Neural Networks, 1993, pp. 501–506.

    Google Scholar 

  10. Caruana, R., Baluja, S., and Mitchell, T.: Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Analysis, Neural Information Processing 7 (1995).

    Google Scholar 

  11. Cestnik, B.: Estimating Probabilities: A Crucial Task in Machine Learning, In: Proc. European Conf. on Artificial Intelligence, Stockholm, 1990, pp. 147–149.

    Google Scholar 

  12. Clark, P. and Boswell, R.: Rule induction with CN2: Some recent improvements. In: Proc. Fifth European Working Session on Learning, Springer, 1991, pp. 151–163.

    Google Scholar 

  13. Clark, P. and Niblett, T.: The CN2 induction algorithm. Machine Learning,3(4):261–283 (1989).

    Google Scholar 

  14. Compton, P. and Jansen, R.: Knowledge in context: A strategy for expert system maintenance. In: Proc. 2nd Australian Joint Artificial Intelligence Conference, Springer LNAI 406, 1988, pp. 292–306.

    Google Scholar 

  15. Compton, P., Horn, R., Quinlan, R. and Lazarus, L.: Maintaining an expert system. In: Applications of Expert Systems (Quinlan, R., ed.), Addison Wesley, 1989, pp. 366–385.

    Google Scholar 

  16. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13: 21–27 (1968).

    Article  Google Scholar 

  17. Dasarathy, B.V., ed.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA, 1990.

    Google Scholar 

  18. Dehaspe, L, Toivonen, H. and King, R.D.: Finding frequent substructures in chemical compounds. In: Proc. 4th International Conference on Knowledge Discovery and Data Mining, (KDD-98) (Agrawal, R., Stolorz, P. and Piatetsky-Shapiro, G., eds.), AAAI Press, 1998, pp. 30–37.

    Google Scholar 

  19. De Raedt, L. and Dehaspe, L.: Clausal discovery. Machine Learning, 26:99–146 (1997).

    Article  MATH  Google Scholar 

  20. Downs, J., Harrison, R.F., Kennedy, R.L., and Cross, S.C.: Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks, Artificial Intelligence in Medicine, 8(4): 403–428 (1996).

    Article  Google Scholar 

  21. Dudani, S.A.: The distance-weighted κ-nearest neighbor rule, IEEE Transactions on Systems, Man and Cybernetics, 6(4): 325–327 (1975).

    Google Scholar 

  22. Džeroski, S. and Lavrač, N.: Rule induction and instance-based learning applied in medical diagnosis, Technology and Health Care, 4(2): 203–221 (1996).

    Google Scholar 

  23. Edwards, G., Compton, P., Malor, R., Srinivasan, A. and Lazarus, L.: PEIRS: A pathologist maintained expert system for the interpretation of chemical pathology reports, Pathology 25: 27–34 (1993).

    Article  Google Scholar 

  24. Fausett, L.V.: Fundamentals of neural networks: Architectures, algorithms and applications, Prentice Hall, Upper Saddle River, NJ, 1994.

    MATH  Google Scholar 

  25. Fix, E., Hodges, J.L.: Discriminatory analysis. Nonparametric discrimination. Consistency properties. Technical Report 4, US Air Force School of Aviation Medicine. Randolph Field, TX, 1957.

    Google Scholar 

  26. Grzymała-Busse, J.: Applications of the rule induction systems LERS, In: [56], 1998, pp. 366–375.

    Google Scholar 

  27. Ham, F.M. and Han, S., Classification of cardiac using fuzzy ARTMAP, IEEE Transactions on Biomedical Engineering, 43(4): 425–430 (1996).

    Article  Google Scholar 

  28. Horn, K., Compton, P.J., Lazarus, L. and Quinlan, J.R.: An expert system for the interpretation of thyroid assays in a clinical laboratory, Austr. Comput. Journal 17(1): 7–11 (1985).

    Google Scholar 

  29. Kahn, C.E., and Anderson, G.M.: Case-based reasoning and imaging procedure selection, Investigative Radiology, 29(6): 643–647 (1994).

    Article  Google Scholar 

  30. Kattan, M.W., Ishida, H., Scardino, P.T. and Beck, J.R.: Applying a neural network to prostate cancer survival data. In: Intelligent data analysis in medicine and pharmacology (Lavrač, N. Keravnou, E. and Zupan, B., eds.), Kluwer, 1997, pp. 295–306.

    Google Scholar 

  31. Kohonen, T.: Self-organization and associative memory, Springer-Verlag, New York, 1988.

    MATH  Google Scholar 

  32. Komorowski, J. and Øhrn, A.: Modelling prognostic power of cardiac tests using rough sets, Artificial Intelligence in Medicine, 1998 (in press).

    Google Scholar 

  33. Kononenko, I.: Semi-naive Bayesian classifier. In: Proc. European Working Session on Learning-91 (Kodratoff, Y., ed.), Porto, Springer, 1991, pp. 206–219.

    Google Scholar 

  34. Kononenko, I.: Inductive and Bayesian learning in medical diagnosis, Applied Artificial Intelligence, 7: 317–337 (1993).

    Article  Google Scholar 

  35. Kononenko, I., Bratko, I., and Kukar, M.: Application of machine learning to medical diagnosis. In Machine Learning and Data Mining: Methods and Applications, R. S. Michalski, I. Bratko, and M. Kubat (Eds.), John Willey and Sons, 1998, pp. 389–408.

    Google Scholar 

  36. Lavrač, N., Džeroski, S., Pirnat, V. and Križman, V.: The utility of background knowledge in learning medical diagnostic rules, Applied Artificial Intelligence, 7: 273–293 (1993).

    Article  Google Scholar 

  37. Lavrač, N. and Džeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994.

    MATH  Google Scholar 

  38. Lavrač, N., Keravnou, E. and Zupan, B., eds.: Intelligent Data Analysis in Medicine and Pharmacology, 1997, Kluwer.

    Google Scholar 

  39. Lavrač, N.: Selected techniques for data mining in medicine. Artificial Intelligence in Medicine, Special Issue on Data Mining Techniques and Applications in Medicine, 1999 (in press).

    Google Scholar 

  40. Lavrač, N., Kononenko, I., Keravnou, E., Kukar, M. and Zupan, B.: Intelligent data analysis for medical diagnosis: Using machine learning and temporal abstraction. AI Communications, 1999 (in press).

    Google Scholar 

  41. Lavrač, N., Flach, P.A. and Zupan, B.: Rule evaluation measures: A unifying view, 1999 (submitted to Int. Workshop on Inductive Logic Programming, ILP-99).

    Google Scholar 

  42. Liestøl, K., Andersen, P.K. and Andersen, U.: Survival analysis and neural nets, Statist. Med., 13: 1189–1200 (1994).

    Article  Google Scholar 

  43. Macura, R.T. and Macura, K., eds.: Case-based reasoning: opportunities and applications in health care, Artificial Intelligence in Medicine, 9(1): 1–4 (1997).

    Google Scholar 

  44. Macura, R.T. and Macura, K., eds.: Artificial Intelligence in Medicine: Special Issue on Case-Based Reasoning, 9(1), 1997.

    Google Scholar 

    Google Scholar 

  45. Mariuzzi, G., Mombello, A., Mariuzzi, L., Hamilton, P.W., Weber, J.E., Thompson D. and Bartels, P.H.: Quantitative study of ductal breast cancer-patient targeted prognosis: an exploration of case base reasoning, Pathology, Research & Practice, 193(8): 535–542 (1997).

    Google Scholar 

  46. McSherry, D.: Hypothesist: A development environment for intelligent diagnostic systems. In: Proc. Sixth Conference on Artificial Intelligence in Medicine (AIME'97), Springer, 1997, pp. 223–234.

    Google Scholar 

  47. McSherry, D.: Avoiding premature closure in sequential diagnosis, Artificial Intelligence in Medicine, 10(3): 269–283 (1997).

    Article  MathSciNet  Google Scholar 

  48. Michalski, R.S.: A theory and methodology of inductive learning. In: Machine Learning: An Artificial Intelligence Approach ( Michalski, R., Carbonell, J. and Mitchell, T.M., eds.), volume I, Palo Alto, CA, Tioga, 1983, pp. 83–134.

    Google Scholar 

  49. Michalski, R., Mozetič, I., Hong, J. and Lavrač, N.: The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proc. Fifth National Conference on Artificial Intelligence, Morgan Kaufmann, 1986, pp. 1041–1045.

    Google Scholar 

  50. Modai, I., Israel, A., Mendel, S., Hines, E.L. and Weizman, R.: Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed in-patients, Journal of Medical Systems, 20(6): 403–412 (1996).

    Article  Google Scholar 

  51. Michie, D., Spiegelhalter, D.J. and Taylor, C.C., eds.: Machine learning, neural and statistical classification, Ellis Horwood, 1994.

    Google Scholar 

  52. Muggleton, S.: Inverse entailment and Progol, New Generation Computing, Special Issue on Inductive Logic Programming, 13(3-4): 245–286 (1995).

    Google Scholar 

  53. Niblett, T. and Bratko, I.: Learning decision rules in noisy domains. In: Research and Development in Expert Systems III (Bramer, M., ed.), Cambridge University Press, 1986, pp. 24–25.

    Google Scholar 

  54. Pawlak, Z.: Information systems-theoretical foundations. Information Systems, 6:205–218 (1981).

    Article  MATH  Google Scholar 

  55. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, volume 9 of Series D: System Theory, Knowledge Engineering and Problem Solving. Kluwer, 1991.

    Google Scholar 

  56. Polkowski, L. and Skowron, A., eds.: Rough Sets in Knowledge Discovery 1: Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing. Physica-Verlag, 1998.

    Google Scholar 

  57. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1): 81–106 (1986).

    Google Scholar 

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Author information

Authors and Affiliations

  1. J. Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Nada Lavrač

Authors
  1. Nada Lavrač
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Editor information

Editors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Werner Horn

  2. Department of Medicine and Computer Science, Stanford University, 251 Campus Drive, Stanford, CA, 94305-5479, USA

    Yuval Shahar

  3. Department of Medicine Karolinska Institutet, Huddinge University Hospital, Sweden

    Greger Lindberg

  4. Department of Medical Informatics and Image Analysis, Aalborg University, Fredrik Bajers Vej 7D, DK-9000, Aalborg, Denmark

    Steen Andreassen

  5. School of Public Policy, University College London, 29/30 Tavistock Square, London, WC1H 9EZ, UK

    Jeremy Wyatt

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Lavrač, N. (1999). Machine Learning for Data Mining in Medicine. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_4

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  • DOI: https://doi.org/10.1007/3-540-48720-4_4

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  • Print ISBN: 978-3-540-66162-7

  • Online ISBN: 978-3-540-48720-3

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