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

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Abstract

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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Acknowledgement

This work described in this paper was supported by Research Grant from National Natural Science Foundation of China (60774103) and Major Program Development Fund of SJTU. Moreover, we would also like to thank to the whole medical staff of Shanghai No. 6 People’s Hospital for real data collecting and helpful discussions.

The authors would like to express sincere appreciation to the journal editor and two anonymous referees for their detailed and helpful comments to improve the quality of the paper.

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Authors and Affiliations

  1. Department of Industrial Engineering & Logistics Management, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China

    Gang Du, Zhibin Jiang & Yan Ye

  2. Shanghai Putuo District Central Hospital, Shanghai, 200062, China

    Xiaodi Diao

  3. Shanghai No. 6 People’s Hospital, Shanghai, 200233, China

    Yang Yao

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  1. Gang Du
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  2. Zhibin Jiang
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  4. Yan Ye
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Correspondence to Zhibin Jiang.

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Du, G., Jiang, Z., Diao, X. et al. Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm. J Med Syst 36, 1283–1300 (2012). https://doi.org/10.1007/s10916-010-9589-6

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  • DOI: https://doi.org/10.1007/s10916-010-9589-6

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