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A simple statistical model for determining the admission or discharge of dyspnea patients  

Park, Cheol-Yong (Department of Statistics, Keimyung University)
Kim, Tae-Yoon (Department of Statistics, Keimyung University)
Kwon, O-Jin (Department of Statistics, Keimyung University)
Park, Hyoung-Seob (Department of Internal Medicine, School of Medicine, Keimyung University)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.2, 2010 , pp. 279-289 More about this Journal
Abstract
In this study, we propose a simple statistical model for determining the admission or discharge of 668 patients with a chief complaint of dyspnea. For this, we use 11 explanatory variables which are chosen to be important by clinical experts among 55 variables. As a modification process, we determine the discharge interval of each variable by the kernel density functions of the admitted and discharged patients. We then choose the optimal model for determining the discharge of patients based on the number of explanatory variables belonging to the corresponding discharge intervals. Since the numbers of the admitted and discharged patients are not balanced, we use, as the criteria for selecting the optimal model, the arithmetic mean of sensitivity and specificity and the harmonic mean of sensitivity and precision. The selected optimal model predicts the discharge if 7 or more explanatory variables belong to the corresponding discharge intervals.
Keywords
Admission or discharge; dyspnea patients; kernel density function; precision; sensitivity; specificity;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Kerber, R. (1992). ChiMerge: Discretization of numeric attribute. Proceedings of the 10th National Conference on Artificial Intelligence (AAAI-92), 123-127.
2 Kim, J. S., Jang, Y. M. and Na, J. H. (2005) Comparison of multiway discretization algorithms for data mining. Journal of the Korean Data & Information Science Society, 16, 801-813.   과학기술학회마을
3 Na, J. H., Kim, J. M. and Cho, W. S. (2005). Comparison of binary discretization algorithms for data mining. Journal of the Korean Data & Information Science Society, 16, 769-780.   과학기술학회마을
4 van Rijsbergen, C. J. (1979). Information retrieval, Butterworths, London.
5 Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6, 7-19.   DOI
6 이상훈, 박정은, 오경환 (2003). 데이터 분포를 고려한 연속 값 속성의 이산화. <한국퍼지 및 지능시스템 학회 논문지>, 13, 391-396.   과학기술학회마을
7 Chawla, N. V., Japkowwicz, N. and Nolcz, A. (2004). Editorial: Special issuse on learning from imbalanced data sets. SIGKDD Explorations, 6, 1-6.   DOI
8 Fayyad, U. M. and Irani, K. B. (1993). Multi-interval discretization of continuous attributes as preprocessing for classification learning. Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1022-1027.
9 Johnson, R. A. and Wichern, D. W. (1992). Applied multivariate statistical analysis, 3rd Ed., Prentice Hall, New Jersey.
10 Jevon, P. and Ewens, B. (2001). Assessment of a breathless patient. Nursing Standard, 15, 48-53.