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Medical consumable usage control based on Canopy_K-means clustering and WARM

Author

Listed:
  • Ying Yang

    (Shanghai Polytechnic University)

  • Huijing Wu

    (Shanghai Jiaotong University)

  • Caixia Yan

    (Shanghai Jiaotong University)

Abstract

Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.

Suggested Citation

  • Ying Yang & Huijing Wu & Caixia Yan, 0. "Medical consumable usage control based on Canopy_K-means clustering and WARM," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-18.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-019-00468-0
    DOI: 10.1007/s10878-019-00468-0
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    References listed on IDEAS

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