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

Authors:
Gang Du;Zhibin Jiang;Xiaodi Diao;Yan Ye;Yang Yao
Affiliations:
Department of Industrial Engineering & Logistics Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China 200240;Department of Industrial Engineering & Logistics Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China 200240;Shanghai Putuo District Central Hospital, Shanghai, China 200062;Department of Industrial Engineering & Logistics Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China 200240;Shanghai No. 6 People's Hospital, Shanghai, China 200233
Venue:
Journal of Medical Systems
Year:
2012

Citing 21
Cited 2

Integrating artificial neural networks with rule-based expert systems

Decision Support Systems - Special issue on neural networks for decision support
Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter

Fuzzy Sets and Systems
Training fuzzy systems with the extended Kalman filter

Fuzzy Sets and Systems - Fuzzy systems
Statistical Analysis of Computational Tests of Algorithms and Heuristics

INFORMS Journal on Computing
Hybrid identification in fuzzy-neural networks

Fuzzy Sets and Systems - Theme: Learning and modeling
An improved fuzzy neural network based on T-S model

Expert Systems with Applications: An International Journal
A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing

Neurocomputing
An efficient immune-based symbiotic particle swarm optimization learning algorithm for TSK-type neuro-fuzzy networks design

Fuzzy Sets and Systems
Pattern recognition using neural-fuzzy networks based on improved particle swam optimization

Expert Systems with Applications: An International Journal
Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter

Fuzzy Sets and Systems
An ontology-based hierarchical semantic modeling approach to clinical pathway workflows

Computers in Biology and Medicine
A new approach to systematization of the management of paper-based clinical pathways

Computer Methods and Programs in Biomedicine
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms

Expert Systems with Applications: An International Journal
A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications

Fuzzy Sets and Systems
Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction

Fuzzy Sets and Systems
Chest diseases diagnosis using artificial neural networks

Expert Systems with Applications: An International Journal
A Cooperative approach to particle swarm optimization

IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

IEEE Transactions on Evolutionary Computation
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive

IEEE Transactions on Fuzzy Systems
Evolutionary fuzzy modeling

IEEE Transactions on Fuzzy Systems
Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms

IEEE Transactions on Fuzzy Systems

Using Recommendation to Support Adaptive Clinical Pathways

Journal of Medical Systems
Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

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.