Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 3/2012

01-06-2012 | ORIGINAL PAPER

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

Published in: Journal of Medical Systems | Issue 3/2012

Login to get access

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.
Literature
1.
Bragato, L., and Jacobs, K., Care pathways: the road to better health services. J. Health Organ. Manage. 17(3):164–180, 2003.CrossRef
2.
Cheah, J., Development and implementation of a clinical pathway programme in an acute care general hospital in Singapore. Int. J. Qual. Health Care 12:403–412, 2000.CrossRef
3.
Hunter, B., and Segrott, J., Re-mapping client journeys and professional identities: a review of the literature on clinical pathways. Int. J. Nurs. Stud. 45(4):608–625, 2008.CrossRef
4.
Wakamiya, S. J., and Yamauchi, K., What are the standard functions of electronic clinical pathways? Int. J. Med. Inform. 78(8):543–550, 2009.CrossRef
5.
Du, G., Jiang, Z. B., Diao, X. D., Ye, Y., Yao, Y., Modeling, variation monitoring, analyzing, reasoning for intelligently reconfigurable clinical pathway. Proceedings of the IEEE International Conference on Service Operations, Logistics and Informatics, 85–90, Chicago, IL, USA, 2009.
6.
Wigfield, A., and Boon, E., Critical care pathway development: the way forward. Br. J. Nurs. 5(12):732–735, 1996.
7.
Cheah, J., Clinical pathways: changing the face of client care delivery in the next millennium. Clinician Manag. 7(78):78–84, 1998.
8.
Price, M. B., Jones, A., Hawkins, J. A., et al., Critical pathways for postoperative care after simple congenital heart surgery. AJMC 5:185–192, 1999.
9.
Atwal, A., and Caldwell, K., Do multidisciplinary integrated care pathways improve interprofessional collaboration? Scand. J. Caring Sci. 16(4):360–367, 2002.CrossRef
10.
Bryan, S., Holmes, S., Prostlethwaite, D., and Carty, N., The role of integrated care pathways in improving the client experience. Prof. Nurse 18(2):77–79, 2002.
11.
Caminiti, C., Scoditti, U., Diodati, F., and Passalacqua, R., How to promote, improve and test adherence to scientific evidence in clinical practice. BMC Health Serv. Res. 5(62):1–11, 2005.
12.
Wakamiya, S., and Yamauchi, K., A new approach to systematization of the management of paper-based clinical pathways. Comput. Meth. Programs Biomed. 82:169–176, 2006.CrossRef
13.
Ye, Y., Jiang, Z. B., Diao, X. D., Yang, D., and Du, G., An ontology-based hierarchical semantic modeling approach to clinical pathway workflows. Comput. Biol. Med. 39(8):722–732, 2009.CrossRef
14.
Ye, Y., Jiang, Z. B., Diao, X. D., Du, G., Knowledge-based hybrid variance handling for patient care workflows based on clinical pathways. Proceedings of the IEEE International Conference on Service Operations, Logistics and Informatics, 13–18, Chicago, IL, USA, 2009.
15.
Ye, Y., Jiang, Z. B., Diao, X. D., Du, G., A Semantics-Based Clinical Pathway Workflow and Variance Management Framework, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, 758–762, Bei Jing, China, 2008.
16.
Du, G., Jiang, Z. B., Diao, X. D., Sun, Y. J., Ye, Y., and Yao, Y., Adaptive workflow engine based on rule for clinical pathway. Journal of Shanghai Jiao Tong University 43(7):1021–1026, 2009.
17.
Er, O., Yumusak, N., and Temurtas, F., Chest diseases diagnosis using artificial neural networks. Expert Syst. Appl. 37(12):7648–7655, 2010.CrossRef
18.
Chen, Y., Yang, B., Abraham, A., and Peng, L., Automatic design of hierarchical Takagi–Sugeno fuzzy systems using evolutionary algorithms. IEEE Trans. Fuzzy Syst. 15(3):385–397, 2007.MATHCrossRef
19.
Yoon, Y., Guimaraes, T., and Swales, G., Integrating artificial neural networks with rule-based expert systems. Decis. Support Syst. 11(5):497–507, 1994.CrossRef
20.
Takagi, T., and Sugeno, M., Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15:116–132, 1985.MATH
21.
Lin, F. J., Lin, C. H., and Shen, P. H., Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE Trans. Fuzzy Sys. 9(5):751–759, 2001.CrossRef
22.
Jang, J. S. R., ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3):665–684, 1993.MathSciNetCrossRef
23.
Pedrycz, W., and Reformat, M., Evolutionary fuzzy modeling. IEEE Trans. Fuzzy Syst. 11(5):652–665, 2003.CrossRef
24.
Oh, S. K., Pedrycz, W., and Park, H. S., Hybrid id entification in fuzzy neural networks. Fuzzy Sets Syst. 138:399–426, 2003.MathSciNetCrossRef
25.
Wang, L., and Yen, J., Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter. Fuzzy Sets Syst. 101:353–362, 1999.MathSciNetCrossRef
26.
Wang, H., Kwong, S., Jin, Y., Wei, W., and Man, K. F., Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst. 149(1):149–186, 2005.MathSciNetMATHCrossRef
27.
Tang, A. M., Quek, C., and Ng, G. S., GA-TSKfnn: parameters tuning of fuzzy neural network using genetic algorithms. Expert Syst. Appl. 29:769–781, 2005.CrossRef
28.
Lin, C. J., and Xu, Y. J., A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications. Fuzzy Sets Syst. 157:1036–1056, 2006.MathSciNetMATHCrossRef
29.
Jelodar, M. S., Kamal, M., Fakhraie, S. M., Ahmadabadi, M. N., SOPC-based parallel genetic algorithm. Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 2006.
30.
Kennedy, J., Eberhart, R C., Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks. Piscataway, 1942–1948, 1995.
31.
Fourie, P. C., and Groenwold, A. A., The particle swarm optimization algorithm in size and shape. Struct. Multidiscip. O. 23(4):259–267, 2002.CrossRef
32.
Han, M., Sun, Y. N., and Fan, Y. N., An improved fuzzy neural network based on T-S model. Expert Syst. Appl. 34:2905–2920, 2008.CrossRef
33.
Khosla, A., Kumar, S., Aggarwal, K. K., A framework for identification of fuzzy models through particle swarm optimization, Proceedings of the IEEE Indicon Conference, 388–391, Chennai, India, 2005.
34.
Khosla, A., Kumar, S., Ghosh, K. R., A comparison of computational efforts between particle swarm optimization and genetic algorithm for identification of fuzzy models, in: Annual Conference of the North American Fuzzy Information Processing Society-NAFIPS, 245–250, 2007.
35.
Shoorehdeli, M. A., Teshnehlab, M., and Sedigh, A. K., Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter. Expert Syst. Appl. 160:922–948, 2009.MathSciNetMATH
36.
Shi, Y., and Eberhart, R. C., Empirical study of particle swarm optimization. Proc. IEEE Int. Conf. Evolutionary Computation 3:101–106, 1999.
37.
Ratnaweera, A., Halgamuge, S. K., and Watoson, H. C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient. IEEE Trans. Evol. Comput. 8(3):240–255, 2004.CrossRef
38.
Lovbjerg, M., Rasmussen, T. K., Krink, T., Hybrid particle swarm optimizer with breeding and sub-populations. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). San Francisco, CA, July 2001.
39.
Xie, X. F., Zhang, W. J., et al., Optimizing semiconductor devices by self-organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA, 2017–2022, 2004.
40.
He, R., Wang, Y., et al., An improved particle swarm optimization based on self-adaptive escape velocity. JSW 16(12):2036–2044, 2005.MATHCrossRef
41.
Lin, C.-J., Wang, J.-G., and Lee, C.-Y., Pattern recognition using neural-fuzzy networks based on improved particle swam optimization. Expert Syst. Appl. 36:5402–5410, 2009.CrossRef
42.
Lin, C.-J., An efficient immune-based symbiotic particle swarm optimization learning algorithm for TSK-type neuro-fuzzy networks design. Fuzzy Sets Syst. 159:2890–2909, 2008.CrossRef
43.
Van den Bergh, F., and Engelbrecht, A. P., A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3):225–239, 2004.CrossRef
44.
Niu, B., Zhu, Y. L., He, X. X., and Shena, H., A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing. Neurocomputing 71:1436–1448, 2008.CrossRef
45.
Higashi, N., Iba, H., Particle swarm optimization with Gaussian mutation. Proceedings of the IEEE Swarm Intelligence Symp. Indianapolis: IEEE Inc, 72–79, 2003.
46.
Kennedy, J., Eberhart, R. C., A discrete binary version of the particle swarm algorithm, systems, man, and cybernetics, Computational Cybernetics and Simulation IEEE International Conference, 5 (12–15): 4104–4108, 1997.
47.
Simon, D., Training fuzzy systems with the extended Kalman filter. Fuzzy Sets Syst. 132:189–199, 2002.MATHCrossRef
48.
Coffin, M., and Saltzman, Statistical analysis of computational tests of algorithms and heuristics. INFORMS J. Comput. 12(1):24–44, 2000.MATHCrossRef
Metadata
Title
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
Publication date
01-06-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 3/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-010-9589-6

Other articles of this Issue 3/2012

Journal of Medical Systems 3/2012 Go to the issue

ORIGINAL PAPER

Mobile Agent Application and Integration in Electronic Anamnesis System

Original Paper

Chinese Acupuncture Expert System (CAES)—A Useful Tool to Practice and Learn Medical Acupuncture

ORIGINAL PAPER

A New Method of Exercising Pandemic Preparedness Through an Interactive Simulation and Visualization

ORIGINAL PAPER

Designing a Decision Support System for Distinguishing ADHD from Similar Children Behavioral Disorders

Original Paper

A Basic Study on Application of Voice Recognition Input to an Electronic Nursing Record System -Evaluation of the Function as an Input Interface-

ORIGINAL PAPER

Analysis of Infant Cry Through Weighted Linear Prediction Cepstral Coefficients and Probabilistic Neural Network