Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm

被引:0
作者
Gang Du
Zhibin Jiang
Xiaodi Diao
Yan Ye
Yang Yao
机构
[1] Shanghai Jiao Tong University,Department of Industrial Engineering & Logistics Management, School of Mechanical Engineering
[2] Shanghai Putuo District Central Hospital,undefined
[3] Shanghai No. 6 People’s Hospital,undefined
来源
Journal of Medical Systems | 2012年 / 36卷
关键词
T-S fuzzy neural networks; Clinical pathway; Particle swarm optimization (PSO); Variances handling;
D O I
暂无
中图分类号
学科分类号
摘要
Clinical pathways’ variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients’ life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs.
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页码:1283 / 1300
页数:17
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