Abstract
Clinical pathways are management plans that display goals for patients and the sequence and timing of actions necessary to achieve those goals with optimal efficiency. Given the resource competition between multiple clinical pathways, clinical pathway scheduling is an urgent and meaningful question to be solved, which is a typical resource constrained multi-project scheduling problem (RCMPSP). Intelligent optimization algorithms are widely used to solve RCMPSP problems. In this paper, considering the shortcomings of the existing optimal scheduling algorithms, which is insufficient in search and easy to fall into local optimum, we construct a model for clinical pathway scheduling and propose a hybrid intelligent optimization algorithm. We also design the encoding and decoding rules and fitness function of the optimization. Experiment results on renal calculi clinical pathway show that the proposed algorithm has a better search capabilities in clinical pathway scheduling and preforms better performance when the number of patients increases.
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Sun, X., Xie, X., Zhang, Y., Cui, J. (2020). Clinical Pathway Optimal Scheduling Based on Hybrid Intelligent Optimization Algorithm. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12307. Springer, Cham. https://doi.org/10.1007/978-3-030-60636-7_38
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DOI: https://doi.org/10.1007/978-3-030-60636-7_38
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