Abstract
This paper presents a methodology based on automatic knowledge discovery that aims to identify and predict the possible causes that makes a patient to be considered of high cost. The experiments were conducted in two directions. The first was the identification of important relationships among variables that describe the health care events using an association rules discovery process. The second was the discovery of precise prediction models of high cost patients, using classification techniques. Results from both methods are discussed to show that the patterns generated could be useful to the development of a high cost patient eligibility protocol, which could contribute to an efficient case management model.
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© 2009 IFIP International Federation for Information Processing
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Kobus, L.S., Enembreck, F., Scalabrin, E.E., Dias, J.d.S., Silva, S.H.d. (2009). Automatic Knowledge Discovery and Case Management: an Effective Way to Use Databases to Enhance Health Care Management. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds) Artificial Intelligence Applications and Innovations III. AIAI 2009. IFIP International Federation for Information Processing, vol 296. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0221-4_29
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DOI: https://doi.org/10.1007/978-1-4419-0221-4_29
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