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More and more medical insurance institutes employ historical medical data as a guide tool, but it is hardly to understand the trends over the time. In the dynamic environment, understanding changes of the datasets can help health care institutions to grasp the basic trends of concurrencies, establish effective measurement of health interventions, and formulate rational decisions. Thus, it is essential to develop change mining methods for efficient mining in medical databases. In this paper, the authors adopted a methodology which detects changes of concurrencies from medical checkup databases at different periods.
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