Mining Surgical Meta-actions Effects with Variable Diagnoses’ Number
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)  to demonstrate how to extract meta-actions and evaluate them.
KeywordsMeta-actions Actionable rules Surgical treatments
Unable to display preview. Download preview PDF.
- 1.Clinical classifications software (ccs) for icd-9-cm, http://www.hcup-us.ahrq.gov
- 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
- 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
- 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
- 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.Yang, Q., Chen, H.: Mining case for action recommendation. In: Proceedings of ICDM, pp. 522–529 (2002)Google Scholar
- 12.Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, Syracuse (2003)Google Scholar