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A hybrid approach of data mining and genetic algorithms for rehabilitation scheduling

Author

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  • Chen-Fu Chien
  • Yi-Chao Huang
  • Chih-Han Hu

Abstract

To enhance the medical care quality and patient satisfaction, the hospital management has received considerable attention. This research aims to develop an intelligent approach that integrates Genetic Algorithm (GA) and Data Mining (DM) approaches to resolve the physical therapy scheduling problems to reduce patient waiting time and thus enhance service quality. In particular, this approach employed the attribute-oriented induction method to extract the patterns of the solutions generated from the GA approach. Thus, the decision rules derived from the patterns can be applied to resolve similar therapy scheduling problems with much lesser computational effort. The results of an empirical study conducted in a general hospital validated the practical viability of this approach.

Suggested Citation

  • Chen-Fu Chien & Yi-Chao Huang & Chih-Han Hu, 2009. "A hybrid approach of data mining and genetic algorithms for rehabilitation scheduling," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 16(1/2), pages 76-100.
    • Handle: RePEc:ids:ijmtma:v:16:y:2009:i:1/2:p:76-100
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    Cited by:

    1. Nossack, Jenny, 2022. "Therapy scheduling and therapy planning at hospitals," Omega, Elsevier, vol. 109(C).
    2. Lizhong Zhao & Chen-Fu Chien & Mitsuo Gen, 2018. "A bi-objective genetic algorithm for intelligent rehabilitation scheduling considering therapy precedence constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 973-988, June.

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