Abstract
In medicine, decision processes are correct not only if they conclude with a right final decision, but also if the sequence of observations that drive the whole process to the final decision defines a sequence with a medical sense. Decision trees are formal structures that have been successfully applied to make decisions in medicine; however, the traditional machine learning algorithms used to induce these trees use information gain or cost ratios that cannot guarantee that the sequences of observations described by the induced trees have a medical sense. Here, we propose a slight variation of classical decision tree structures, provide four quality ratios to measure the medical correctness of a decision tree, and introduce a machine learning algorithm to induce medical decision trees whose final decisions are both correct and the result of a sequence of observations with a medical sense. The algorithm has been tested with four medical decision problems, and the successful results discussed.
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References
Campello, R.J.G.B., Hruschka, E.R.: On comparing two sequences of numbers andits applications to clustering analysis. Information Sciences 179(8), 1025–1039 (2009)
Fauci, A.S., Braunwald, E., Kasper, D.L., Hauser, S.L., Longo, D.L., Jameson, J.L., Loscalzo, J. (eds.): Harrison’s Principles of Internal Medicine, 17th edn. McGraw Hill, New York (2008)
Herbst, J.: A machine learning approach to workflow management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000)
Kokol, P., Zorman, M., Stiglic, M.M., Malcic, I.: The limitations of decision trees and automatic learning in real world medical decision making. In: Proc. of the 9th World Congress on Medical Informatics, pp. 529–533 (1998)
Ling, C.X., Sheng, V.S., Yang, Q.: Test Strategies for Cost-Sensitive Decision Trees. IEEE Transaction on Knowledge and Data Engineering 18(8), 1055–1067 (2006)
López-Vallverdú, J.A., Riaño, D., Collado, A.: Increasing acceptability of decision trees with domain attributes partial orders. In: Proc. of the 20th IEEE CBMS, Maribor, Slovenia, pp. 569–574 (2007)
López-Vallverdú, J.A., Riaño, D., Bohada, J.A.: Health-Care criteria to improve the induction of decision mechanisms in medicine (submitted)
Mani, S., Aliferis, C.: A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 446–450. Springer, Heidelberg (2007)
Mani, S., Valtorta, M., McDermott, S.: Building Bayesian Network Models in Medicine: The MENTOR Experience. Applied Intelligence 22, 93–108 (2005)
Mushlin, S.B., Greene, H.L.: Decision Making in Medicine: An Algorithmic Approach, 3rd edn. Mosby Elsevier (2010)
Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.: Decision trees: an overview and their use in medicine. J. Med.Syst. 26(5), 445–463 (2002)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Sackett, D.L.: Evidence based medicine: what it is and what it isn’t. BMJ 312(7023), 71–72 (1996)
Sun, R., Giles, C.L.: Sequence Learning: From Recognition and Prediction to Sequential Decision Making. IEEE Intelligent Systems 16(4), 67–70 (2001)
van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workow mining: a survey of issues and approaches. Data and Knowledge Engineering 47, 237–267 (2003)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
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Torres, P., Riaño, D., López-Vallverdú, J.A. (2011). Inducing Decision Trees from Medical Decision Processes. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds) Knowledge Representation for Health-Care. KR4HC 2010. Lecture Notes in Computer Science(), vol 6512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18050-7_4
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DOI: https://doi.org/10.1007/978-3-642-18050-7_4
Publisher Name: Springer, Berlin, Heidelberg
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