Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital

  • Authors:

  • W. T. Lin;Y. C. Wu;J. S. Zheng;M. Y. Chen

  • Affiliations:

  • Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taiwan;Department of Industrial Engineering, Chung Yuan Christian University, Taiwan;Department of Industrial Engineering, Chung Yuan Christian University, Taiwan;Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

''Emergency medicine'' is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients' seriousness through their judgment. This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.