ArticleJournal of medical systems2012
Variances handling method of clinical pathways based on T-S fuzzy neural networks with novel hybrid learning algorithm.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 18 citations in OpenAlex.
- Modified Needleman-Wunsch algorithm for clinical pathway clustering.Journal of biomedical informatics · 2021Article
- An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation.Scientific reports · 2019Article
- A novel adjustment method for shearer traction speed through integration of T-S cloud inference network and improved PSO.Computational intelligence and neuroscience · 2014Article
- Using recommendation to support adaptive clinical pathways.Journal of medical systems · 2012Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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