A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:69752", author = "R. J. Kuo and W. C. Cheng and W. C. Lien and T. J. Yang", title = "A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis", 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.", volume = "9", number = "5", pages = "377-6", }
