A medical cost estimation with fuzzy neural network of acute hepatitis patients in emergency room. Issue 1 (October 2015)

• Record Type: Journal Article
• Authors: Kuo, R.J.; Cheng, W.C.; Lien, W.C.; Yang, T.J.
• Is Part Of: Computer methods and programs in biomedicine. Volume 122:Issue 1(2015)
• Page Start: 40
• Page End: 46
• Publication Date: 2015-10
• Subjects: Acute hepatitis -- Medical resource estimation -- Fuzzy neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.06.006
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: FNN is employed to develop the cost forecasting model for acute hepatitis patients in emergency room. The FNN model can provide more accurate forecast than SVR and ANN. Unlike SVR and ANN, FNN can also provide fuzzy IF–THEN rules for interpretation. Abstract: Taiwan is an area where chronic hepatitis is endemic. Liver cancer is so common that it has been ranked first among cancer mortality rates since the early 1980s in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death. Therefore, as shown by the active research on hepatitis, it is not only a health threat, but also a huge medical cost for the government. The estimated total number of hepatitis B carriers in the general population aged more than 20 years old is 3, 067, 307. Thus, a case record review was conducted from all patients with diagnosis of acute hepatitis admitted to the Emergency Department (ED) of a well-known teaching-oriented hospital in Taipei. The cost of medical resource utilization is defined as the total medical fee. In this study, a fuzzy neural network is employed to develop the cost forecasting model. A total of 110 patients met the inclusion criteria. The computational results indicate that the FNN model can provide more accurate forecasts than the support vector regression (SVR) or artificial neural network (ANN). In addition, unlike SVR and ANN, FNN can also provide fuzzy IF–THEN rules for interpretation.