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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-18050-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18049-1
Online ISBN: 978-3-642-18050-7
eBook Packages: Computer ScienceComputer Science (R0)