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Variances Handling for Clinical Pathway Based on Takagi-Sugeno FNNs with Random Cooperative Decomposing PSO Optimization
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DU Gang1,JIANG Zhibin1,DIAO Xiaodi2,YAO Yang3
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(1.Department of Industrial Engineering & Logistics Management, Shanghai Jiaotong University, Shanghai 200240, China; 2.Shanghai Putuo District Central Hospital, Shanghai 200062, China; 3.Shanghai No.6 People’s Hospital, Shanghai 200233, China)
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Abstract A variances handling method for clinical pathway was proposed, which is based on TakagiSugeno (TS) fuzzy neural networks (FNNs) with random cooperative decomposing particle swarm optimization (RCDPSO). During the process of cooperative coevolution with random execution sequence, a decomposing algorithm was adopted for the particles with the highest performance, and crossover and mutations were adopted for the particles with the worst performance. Therefore, it not only ensures the convergence rate, but also improves performance of the algorithm in global search. Moreover, the variation disturbing mechanism was introduced to strengthen the diversity of population and avoid plunging into local optimum. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy was used to validate the proposed method. The result demonstrates that TS FNNs based on the RCDPSO achieves superior performance in prediction and robustness to TS FNNs based on other algorithms, which makes variances handling of clinical pathway more effective.
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Received: 15 October 2009
Published: 31 August 2010
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