An Online Chronic Diseases Consulting System: A Hyper Heuristic Algorithm Using Random and Greedy Strategy for Complex Scheduling Problems

Description:
This study attempts to develop an online chronic diseases consulting system by using a customized heuristic algorithm for complex scheduling of medical experts to consult patients in a major hospital.

How to cite:

Wen T. Wang H, Hsieh MF, Xie L, Wang D, Luo W, Dong H. An Online Chronic Diseases Consulting System: A Hyper Heuristic Algorithm Using Random and Greedy Strategy for Complex Scheduling Problems. Journal of Medical Imaging and Health Informatics. 2016; 6(1): 233-239. Available at: http://www.ingentaconnect.com/content/asp/jmihi/2016/00000006/00000001/art00031

Methods: We proved this problem is NP-complete problem and used heuristic algorithms to solve it. When the data set is small, most existing algorithms can reach the optimal solution using linear programming. However, traditional greedy algorithm and off-trap strategy fail to give reasonable results in large data set. In this study, we used the algorithm with appropriate oblivion strategy for efficient convergence and optimal solution. Results: To compare different algorithms, synthetic data sets of different size and a year's clinical data set provided by the hospital were used. The outcome of our algorithm was closely matched to the optimal solution from linear programming for sixty synthetic data sets. In addition, our algorithm is more efficient than that of linear programming when clinical data set was used. Meanwhile we found that the outcome is an approximate optimal solution and the algorithm is able to save a lot of cost for the hospital in practice. Conclusions: In this paper, we analyzed the results obtained from the algorithms of data set of different size and found that the algorithm can handle large volumes of data efficiently and reduce cost of hospitals.

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