A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis

Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

Abstract:

Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: Acute hepatitis, Medical resource cost, Artificial neural network, Support vector regression.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100687

References:

[1] W. M. Lee, "Hepatitis B virus infection," New England journal of medicine, vol. 337, pp. 1733-1745, 1997.
[2] S. R. Tujios and W. M. Lee, "New advances in chronic hepatitis B," Current Opinion in Gastroenterology, vol. 28, p. 193, 2012.
[3] C.-H. Chen, P.-M. Yang, G.-T. Huang, H.-S. Lee, J.-L. Sung, and J.-C. Sheu, "Estimation of Seroprevalence of Hepatitis B Virus and Hepatitis C Virus in Taiwan from a Large-scale Survey of Free Hepatitis Screening Participants," Journal of the Formosan Medical Association, vol. 106, pp. 148-155, // 2007.
[4] H. L. Y. Chan, S. W. C. Tsang, Y. Hui, N. W. Y. Leung, F. K. L. Chan, and J. J. Y. Sung, "The role of lamivudine and predictors of mortality in severe flare-up of chronic hepatitis B with jaundice," Journal of Viral Hepatitis, vol. 9, pp. 424-428, 2002.
[5] M. Kumar, S. Jain, B. Sharma, and S. Sarin, "Differentiating Acute Hepatitis B from the First Episode of Symptomatic Exacerbation of Chronic Hepatitis B," Digestive Diseases and Sciences, vol. 51, pp. 594-599, 2006/03/01 2006.
[6] E. Orenbuch-Harroch, L. Levy, and E. Ben-Chetrit, "Acute hepatitis B or exacerbation of chronic hepatitis B-that is the question," World journal of gastroenterology: WJG, vol. 14, p. 7133, 2008.
[7] G. L. Davis and J. H. Hoofnagle, "Reactivation of Chronic Type B Hepatitis Presenting as Acute Viral Hepatitis," Annals of Internal Medicine, vol. 102, pp. 762-765, 1985.
[8] G. Fattovich, L. Brollo, A. Alberti, G. Realdi, P. Pontisso, G. Giustina, et al., "Spontaneous reactivation of hepatitis B virus infection in patients with chronic type B hepatitis," Liver, vol. 10, pp. 141-146, 1990.
[9] I. S. Sheen, Y. F. Liaw, D. I. Tai, and C. M. Chu, "Hepatic decompensation associated with hepatitis B e antigen clearance in chronic type B hepatitis," Gastroenterology, vol. 89, pp. 732-735, 10/ 1985.
[10] P. S. Kamath, R. H. Wiesner, M. Malinchoc, W. Kremers, T. M. Therneau, C. L. Kosberg, et al., "A model to predict survival in patients with end-stage liver disease," Hepatology, vol. 33, pp. 464-470, 2001.
[11] M. Sheth, M. Riggs, and T. Patel, "Utility of the Mayo End-Stage Liver Disease (MELD) score in assessing prognosis of patients with alcoholic hepatitis," BMC Gastroenterology, vol. 2, p. 2, 2002.
[12] Q. Li, G.-Y. Yuan, K.-C. Tang, G.-W. Liu, R. Wang, and W.-K. Cao, "Prognostic factors for chronic severe hepatitis and construction of a prognostic model," Hepatobiliary Pancreat Dis Int, vol. 7, pp. 40-44, 2008.
[13] S. K. Sarin, A. Kumar, J. A. Almeida, Y. K. Chawla, S. T. Fan, H. Garg, et al., "Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific Association for the study of the liver (APASL)," Hepatology international, vol. 3, pp. 269-282, 2009.
[14] S. Sarin, A. Kumar, and H. Garg, "Clinical profile of acute on chronic liver failure (ACLF) and predictors of mortality: a study of 64 patients," Hepatology, vol. 48, p. 450A, 2008.
[15] C. Zauner, R. Apsner, A. Kranz, L. Kramer, C. Madl, B. Schneider, et al., "Outcome prediction for patients with cirrhosis of the liver in a medical ICU: a comparison of the APACHE scores and liver-specific scoringsystems," Intensive care medicine, vol. 22, pp. 559-563, 1996.
[16] Y. C. Huang, "The application of data mining to explore association rules between metabolic syndrome and lifestyles," The HIM journal, 2013.
[17] H. Abdi and L. J. Williams, "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, pp. 433-459, 2010.
[18] G. P. Zhang, B. E. Patuwo, and M. Y. Hu, "A simulation study of artificial neural networks for nonlinear time-series forecasting," Computers & Operations Research, vol. 28, pp. 381-396, 4// 2001.
[19] A. S. Weigend and D. E. Rumelhart, Generalization through minimal networks with application to forecasting: Defense Technical Information Center, 1992.
[20] R. Fildes, K. Nikolopoulos, S. F. Crone, and A. A. Syntetos, "Forecasting and operational research: A review," Journal of the Operational Research Society, vol. 59, pp. 1150-1172, // 2008.
[21] C. J. Lu and Y. W. Wang, "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, vol. 128, pp. 603-613, // 2010.
[22] M. H. Zheng, K. Q. Shi, X. F. Lin, D. D. Xiao, L. L. Chen, W. Y. Liu, et al., "A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network," Journal of viral hepatitis, 2012.
[23] M. Castro-Neto, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, "Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions," Expert systems with applications, vol. 36, pp. 6164-6173, 2009.
[24] C.-J. Lu, T.-S. Lee, and C.-C. Chiu, "Financial time series forecasting using independent component analysis and support vector regression," Decision Support Systems, vol. 47, pp. 115-125, 2009.
[25] P.-F. Pai, S.-L. Yang, and P.-T. Chang, "Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms," Expert Systems with Applications, vol. 36, pp. 10746-10751, 2009.
[26] L.-J. Cao and F. E. H. Tay, "Support vector machine with adaptive parameters in financial time series forecasting," Neural Networks, IEEE Transactions on, vol. 14, pp. 1506-1518, 2003.
[27] V. Vapnik, The nature of statistical learning theory: springer, 2000.
[28] C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, pp. 1-27, 2011.