Mining electronic medical records for patient care patterns

Anna L. Buczak, Linda J. Moniz, Brian H. Feighner, Joseph S. Lombardo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel approach for generating full Electronic Medical Records of synthetic victims is described. Special emphasis is put on the data mining steps that build patient care models and perform clustering of this highly dimensional data set. A methodology for cluster validation is proposed. Results for a large data set with Staphylococcus aureus and Methicillin- Resistant Staphylococcus aureus infections are presented.

Original languageEnglish (US)
Title of host publication2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
Pages146-153
Number of pages8
DOIs
StatePublished - Jul 20 2009
Event2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Nashville, TN, United States
Duration: Mar 30 2009Apr 2 2009

Publication series

Name2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings

Other

Other2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009
CountryUnited States
CityNashville, TN
Period3/30/094/2/09

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software

Cite this

Buczak, A. L., Moniz, L. J., Feighner, B. H., & Lombardo, J. S. (2009). Mining electronic medical records for patient care patterns. In 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings (pp. 146-153). [4938642] (2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings). https://doi.org/10.1109/CIDM.2009.4938642