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

Ying Yang (), Huijing Wu () and Caixia Yan ()
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Ying Yang: Shanghai Polytechnic University
Huijing Wu: Shanghai Jiaotong University
Caixia Yan: Shanghai Jiaotong University

Journal of Combinatorial Optimization, No 0, 18 pages

Abstract: 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.

Keywords: Medical consumable management; Medical consumable usage control; CANOPY_K-means clustering; WARM (search for similar items in EconPapers)
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DOI: 10.1007/s10878-019-00468-0

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