Abstract
Clinical Decision Support Systems embed data-driven decision models designed to represent clinical acumen of an experienced physician. We argue that eliminating physicians’ diagnostic biases from data improves the overall quality of concepts, which we represent as decision rules. Experiments conducted on prospectively collected clinical data show that analyzing this filtered data produces rules with better coverage, certainty and confirmation. Cross-validation testing shows improvement in classification performance.
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References
Arocha, J.F., Wang, D., Patel, V.: Identifying reasoning strategies in medical decision making: A methodological guide. Biomedical Informatics 38(2), 154–171 (2005)
Farion, K., Michalowski, W., Rubin, S., Wilk, S., Correll, R., Gaboury, I.: Prospective evaluation of the MET-AP system providing triage plans for acute pediatric abdominal pain. Int. Journal of Medical Informatics 77(3), 208–218 (2008)
Farion, K., Michalowski, W., Wilk, S., O’Sullivan, D., Matwin, S.: A tree-based decision model to support prediction of the severity of asthma exacerbations in children. Journal of Medical Systems (2009) (forthcoming)
Greco, S., Pawlak, Z., Slowinski, R.: Can Bayesian confirmation measures be useful for rough set decision rules? Engineering App. of AI 17, 345–361 (2004)
Hine, M.J., Farion, K., Michalowski, W., Wilk, S.: Decision Making By Emergency Room Physicians And Residents: Implications for the Design of Clinical Decision Support Systems. International Journal of Healthcare Information Systems and Informatics 4(2), 17–35 (2009)
Kubat, M., Holte, R.C., Matwin, S.: Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning 30(2-3), 195–215 (1998)
Musen, M.A., Sahar, Y., Shortliffe, E.H.: Clinical decision support systems. In: Medical Informatics, Computer applications in healthcare and biomedicine, (nn d edn.), pp. 574–609. Springer, New York
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1998)
Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)
Prietula, M.J., Simon, H.: The experts in your midst. Harvard Business Review 67(1), 120–124 (1989)
Stefanowski, J.: The rough set based rule induction technique for classification problems. In: Proceedings of Sixth European Conference on Intelligent Techniques and Soft Computing, EUFIT 1998, pp. 109–113 (1998)
Stefanowski, J.: Algorithms of rule induction for knowledge discovery. Habilitation Thesis published as Series Rozprawy, vol. 361. Poznan University of Technology Press, Poznan (2001) (in Polish)
Stefanowski, J.: On combined classifiers, rule induction and rough sets. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J.W., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 329–350. Springer, Heidelberg (2007)
Wilk, S., Slowinski, R., Michalowski, W., Greco, S.: Supporting triage of children with abdominal pain in the emergency room. European Journal of Operational Research 160, 696–709 (2005)
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Klement, W., Wilk, S., Michalowski, M., Farion, K. (2010). Experienced Physicians and Automatic Generation of Decision Rules from Clinical Data. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_23
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DOI: https://doi.org/10.1007/978-3-642-13529-3_23
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