Advertisement

Mining Surgical Meta-actions Effects with Variable Diagnoses’ Number

  • Hakim Touati
  • Zbigniew W. Raś
  • James Studnicki
  • Alicja A. Wieczorkowska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

Commonly, information systems are organized by the use of tables that are composed of a fixed number of columns representing the information system’s attributes. However, in a typical hospital scenario, patients may have a variable number of diagnoses and this data is recorded in the patients’ medical records in a random order. Treatments are prescribed based on these diagnoses, which makes it harder to mine meta-actions from healthcare datasets. In such scenario, the patients are not necessarily followed for a specific disease, but are treated for what they are diagnosed for. This makes it even more complex to prescribe personalized treatments since patients react differently to treatments based on their state (diagnoses). In this work, we present a method to extract personalized meta-actions from surgical datasets with variable number of diagnoses. We used the Florida State Inpatient Databases (SID), which is a part of the Healthcare Cost and Utilization Project (HCUP) [1] to demonstrate how to extract meta-actions and evaluate them.

Keywords

Meta-actions Actionable rules Surgical treatments 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Clinical classifications software (ccs) for icd-9-cm, http://www.hcup-us.ahrq.gov
  2. 2.
    Raś, Z.W., Wieczorkowska, A.A.: Action-rules: How to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Wang, K., Jiang, Y., Tuzhilin, A.: Mining actionable patterns by role models. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, pp. 16–26 (2006)Google Scholar
  4. 4.
    Raś, Z.W., Dardzińska, A.: Action rules discovery based on tree classifiers and meta-actions. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS, vol. 5722, pp. 66–75. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Touati, H., Kuang, J., Hajja, A., Raś, Z.W.: Personalized action rules for side effects object grouping. International Journal of Intelligence Science (IJIS) 3(1A), 24–33 (2013); Special Issue on “Knowledge Discovery”, G. Wang (Ed.)Google Scholar
  6. 6.
    Touati, H., Ras, Z.W.: Mining meta-actions for action rules reduction. Fundamenta Informaticae 127(1-4), 225–240 (2013)zbMATHGoogle Scholar
  7. 7.
    Raś, Z.W., Dardzinska, A., Tsay, L.S., Wasyluk, H.: Association action rules. In: IEEE International Conference on Data Mining Workshops, ICDMW 2008, pp. 283–290 (2008)Google Scholar
  8. 8.
    Qiao, Y., Zhong, K., Wang, H., Li, X.: Developing event-condition-action rules in real-time active database. In: Proceedings of the 2007 ACM Symposium on Applied Computing, SAC 2007, pp. 511–516. ACM, New York (2007)CrossRefGoogle Scholar
  9. 9.
    Rauch, J., Šimůnek, M.: Action rules and the guha method: Preliminary considerations and results. In: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems, ISMIS 2009, pp. 76–87. Springer (2009)Google Scholar
  10. 10.
    Yang, Q., Chen, H.: Mining case for action recommendation. In: Proceedings of ICDM, pp. 522–529 (2002)Google Scholar
  11. 11.
    Pawlak, Z.: Information systems - theoretical foundations. Information Systems Journal 6, 205–218 (1981)CrossRefzbMATHGoogle Scholar
  12. 12.
    Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, Syracuse (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hakim Touati
    • 1
  • Zbigniew W. Raś
    • 1
    • 2
  • James Studnicki
    • 3
  • Alicja A. Wieczorkowska
    • 4
  1. 1.College of Comp. and InformaticsUniv. of North CarolinaCharlotteUSA
  2. 2.Inst. of Computer ScienceWarsaw Univ. of TechnologyWarsawPoland
  3. 3.College of Health and Human Serv.Univ. of North CarolinaCharlotteUSA
  4. 4.Polish-Japanese Institute of Information TechnologyWarsawPoland

Personalised recommendations