Automatic Knowledge Discovery and Case Management: an Effective Way to Use Databases to Enhance Health Care Management

  • Luciana SG Kobus
  • Fabrício Enembreck
  • Edson Emílio Scalabrin
  • Joãoda da Silva Dias
  • Sandra Honorato da Silva
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


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.


Case Management Association Rule MYOCARDIC Revascularization Tuple Space Procedure Utilization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Luciana SG Kobus
    • 1
  • Fabrício Enembreck
    • 2
  • Edson Emílio Scalabrin
    • 1
    • 2
  • Joãoda da Silva Dias
    • 1
  • Sandra Honorato da Silva
    • 1
  1. 1.Graduate Program on Health TechnologyPontifical Catholic University of ParanáParanáBrazil
  2. 2.Graduate Program on Applied InformaticsPontifical Catholic University of ParanáParanáBrazil

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