Variances handling method of clinical pathways based on t-s fuzzy neural networks with novel hybrid learning algorithm

Journal of Medical Systems

Volumn 36, Issue 3, 2012, Pages 1283-1300

  Du, Gang ^a   Jiang, Zhibin ^a   Diao, Xiaodi ^b   Ye, Yan ^a   Yao, Yang ^c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Shanghai Putuo District Central Hospital   (China)

^c Shanghai No 6 People's Hospital   (China)

Author keywords

Clinical pathway; Particle swarm optimization (PSO); T S fuzzy neural networks; Variances handling

Indexed keywords

ALANINE AMINOTRANSFERASE; ASPARTATE AMINOTRANSFERASE; FOLINATE CALCIUM; METHOTREXATE;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BONE MARROW SUPPRESSION; CANCER CHEMOTHERAPY; CASE REPORT; CLINICAL PATHWAY; DISCRETE BINARY VERSION OF THE PARTICLE SWARM ALGORITHM; DRUG MEGADOSE; FUZZY SYSTEM; HUMAN; KIDNEY INJURY; LEARNING ALGORITHM; LIVER INJURY; LIVER TOXICITY; OSTEOSARCOMA; PREOPERATIVE PERIOD; RANDOM COOPERATIVE DECOMPOSING PARTICLE SWARM OPTIMIZATION ALGORITHM; RASH; STANDARD; STOMATITIS; VALIDATION PROCESS; VARIANCE;

ALGORITHMS; CRITICAL PATHWAYS; FUZZY LOGIC; HUMANS; NEURAL NETWORKS (COMPUTER);

EID: 84864057710     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9589-6     Document Type: Article

References (48)

1. Bragato, L., and Jacobs, K., Care pathways: the road to better health services. J. Health Organ. Manage. 17(3):164-180, 2003.
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.
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.
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.
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. (Pubitemid 29102589)
9. Atwal, A., and Caldwell, K., Do multidisciplinary integrated care pathways improve interprofessional collaboration? Scand. J. Caring Sci. 16(4):360-367, 2002. (Pubitemid 35417413)
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.
13. Ye, Y., Jiang, Z. B., Diao, X. D., Yang, D., and Du, G., An ontologybased hierarchical semantic modeling approach to clinical pathway workflows. Comput. Biol. Med. 39(8):722-732, 2009.
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.
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. (Pubitemid 46971551)
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.
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.
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.
22. Jang, J. S. R., ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3):665-684, 1993.
23. Pedrycz, W., and Reformat, M., Evolutionary fuzzy modeling. IEEE Trans. Fuzzy Syst. 11(5):652-665, 2003.
24. Oh, S. K., Pedrycz, W., and Park, H. S., Hybrid id entification in fuzzy neural networks. Fuzzy Sets Syst. 138:399-426, 2003.
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.
26. Wang, H., Kwong, S., Jin, Y., Wei, W., and Man, K. F., Multiobjective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst. 149(1):149-186, 2005.
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. (Pubitemid 41394450)
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.
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. (Pubitemid 35354676)
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. (Pubitemid 351173796)
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. (Pubitemid 46276121)
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. (Pubitemid 47533968)
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.
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., Selforganizing hierarchical particle swarm optimizer with timevarying acceleration coefficient. IEEE Trans. Evol. Comput. 8 (3):240-255, 2004.
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.
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.
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.
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.
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. (Pubitemid 351373189)
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. (Pubitemid 35290971)
48. Coffin, M., and Saltzman, Statistical analysis of computational tests of algorithms and heuristics. INFORMS J. Comput. 12(1):24-44, 2000.