Clinical pathway variance prediction using artificial neural network for acute decompensated heart failure clinical pathway
DOI:
https://doi.org/10.11113/mjfas.v14n1.951Keywords:
Artificial Neural Network, Acute Decompensated Heart Failure, Variance Prediction, Clinical PathwayAbstract
Patients in modern healthcare demand superior healthcare quality. Clinical pathways are introduced as the main tools to manage this quality. A clinical pathway is a task-oriented care plan that specifies steps to be taken for patient care. It follows the clinical course according to the specific clinical problem. During clinical pathway execution, variance or deviation from the specified care plan could occur, and may endanger the patient’s life. In this paper, a proposed framework for artificial neural networks (ANNs) in clinical pathway variance predictions is presented. This proposed research method predicts the variance that may occur during Acute Decompensated Heart Failure Clinical Pathway. By using the Artificial Neural Network, 3 variances (Dialysis, PCI, and Cardiac Catherization) are predicted from 55 input. The results show that artificial neural networks with the Levenberg-Marquadt training algorithm with a 55-27-27-1 architecture achieve the best prediction rate, with an average prediction accuracy of 87.4425% for the training dataset and 85.255% for the test dataset.
References
Beale, Hudson, M., Hagan, M. T., Demuth, H. B. 2012. Neural network toolbox™ user’s guide. In R2012a, The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098,, www. mathworks. com. 2012.
Buerkle, T., Baur, T., Hoess, N. 2006. Clinical pathways development and computer support in the EPR: Lessons learned. In Hasman, A., Haux, R., Van Der Lei, J. (Eds.) Ubiquity: Technologies for Better Health in Aging Societies Proceedings of MIE 2006. 27–30 August. Bathmen, NDL: IOS Press, Amsterdam, 1025–1030.
Chu, S., Cesnik, B. 1998. Modelling computerized clinical pathways, In. Cesnik, B., Alexa, T., McCray, J., Scherrer, R. (Eds.). Proceedings of Medinfo’98. 17–22 August, Seoul, Korea: IOS Press: 559–563.
Chu, S., Cesnik, B. 1998. Improving clinical pathway design: Lessons learned from a computerized prototype. International Journal of Medical Informatics 51, 1–11.
Chu, S. 2001.Computerized clinical pathway management systems and the implications. Collegian, 8(2), 19–24.
DiJerome, L. 1992. The nursing case management computerized system: Meeting the challenge of health care delivery through technology. Computers in Nursing, 10(6), 250-258.
Du, G., Jiang, Z., Diao, X., Yao, Y. 2013. Intelligent ensemble T–S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances. Computers in Biology and Medicine, 43(6), 613-634.
Filippo, A., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., Havel, J. 2013. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), 47-58.
Gang, D., Jiang, Z., Diao, X., Ye, Y., Yang, Y. 2012. Variances handling method of clinical pathways based on ts fuzzy neural networks with novel hybrid learning algorithm. Journal of Medical Systems 36(3), 1283-1300.
Gang, D., Jiang, Z., Diao, X., Yao, Y. Knowledge extraction algorithm for variances handling of CP using integrated hybrid genetic double multi-group cooperative PSO and DPSO. Journal of Medical Systems, 36(2), 979-994.
Guoqiang, Z., Patuwo, B. E., Hu, M. Y. 1998. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting 14(1), 35-62.
Hagan, M. T., Demuth, H. B., Beale, M. 1997. Neural Network Design. Boston, MA: PWS Publishing Co.
Hao, A.T., Hsu, C.Y., Li-Fang, H., Jian, W.S., Wu, L. B., Kao, C.C., et al. 2006. Apply creative thinking of decision support in electrical nursing record. Studies in Health Technology and Informatics, 124, 313–319.
Hao, A. T., Huang, L. F., Wu, L. B., Kao, C.C., Lu, M. S., Jian, W.S., Chang, H. K., Hsu, C. Y. 2006. Building an innovation electronic nursing record pilot structure with nursing clinical pathway. Studies in Health Technology and Informatics 122, 279–283.
Hofmann, P. A. 1993. Critical path methods. An important tool for coordinating clinical care. Journal on Quality Improvement, 19, 235-246.
Hurley. K. F., Abidi, S. S. R. 2007. Ontology engineering to model clinical pathways towards the computerization and execution of clinical pathways. 20th IEEE International Symposium on Computer-Based Medical System. 20-22 June. Maribor: IEEE, 536-541.
Hyett, K. L., Podosky, M., Santamaria, N., Ham, J. C. 2007. Valuing variance: The importance of variance analysis in clinical pathways utilisation. Australian Health Review, 31(4), 565-570.
Møller, Fodslette, M. 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525-533.
Okada, O., Ohboshi, N., Uroda, T., Nagase, K. Yoshihara, H. 2005. Electronic clinical path system based seistructured data model using personal digital assistant for onsite access. Journal of Medical Systems, 29(4), 379–389.
Remzi, M., Anagnostou, T., Ravery, V., Zlotta, A., Stephan, C., Marberger, M., Djavan, B. 2003. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology, 62(3), 456-460.
Tu, J. V. 1996. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiol, 49, 1225-1231.
Wakamiya, Shunji, Yamauchi, K. 2006. A new approach to systematization of the management of paper-based clinical pathways. Computer Methods and Programs in Biomedicine, 82(2), 169-176.
Watarai, Y., Kano, N., Fukuma, E., Kameda, T., Bushi, A., Kuroki, M., et al. 2001. Effectiveness of navigation care map. Geka Chiryo 85(3), 271–277. (in Japanese).
Xiang, L., Liu, H., Zhang, S., Mei, J., Xie, G., Yu, Y., Li, J., Lakshmanan, G. T. 2013. Automatic variance analysis of multistage care pathways. Studies in Health Technology and Informatics, 205,715-719.
Yan, Y., Jiang, Z., Diao, X., Du, G. 2009. A knowledge-based variance management system for supporting the implementation of clinical pathways. International Conference on Management and Service Science. 20-22 September. Wuhan, 1-4.
Ye, Y., Jiang, Z., Diao, X., Du, G. 2008. A semantics-based clinical pathway workflow and variance management framework. 2008 IEEE International Conference on Service Operations and Logistics, and Informatics. 12-15 October. Beijing: IEEE, 758-763.
Zhen, H., Jing-Song, L., Hai-Yan, Y., Xiao-Guang, Z. 2009. Modeling of clinical pathways based on ontology. 14-16 August. IEEE International Symposium on IT in Medicine & Education (ITME). Jinan: IEEE, 1170-1174, August 2009.
