转化分布在医院住院时间中的应用

IF 0.6 Q4 STATISTICS & PROBABILITY
S. Harini, M. Subbiah, M. R. Srinivasan, M. Gallo
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引用次数: 0

摘要

住院时间主要表现为不对称、右倾和钩端性。早期的研究考虑了参数分布,如伽马,帕累托,对数正态分布来研究患者在医院的住院时间。然而,在这项研究中,我们提出转换分布是表征停留时间长短的最佳选择。在本研究中,我们考虑了儿童哮喘数据集,并确定转化Weibull-Pareto是最适合的。为了便于比较,我们还提供了伽玛分布、对数正态分布和帕累托分布的结果。采用最大似然法估计变换后分布的未知参数,然后采用拟合优度检验检验拟合分布的适宜性。研究结果为建立因不同医疗问题而需要住院治疗的住院时间模型提供了方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Application of Transformed Distribution: Length of Stay in Hospitals
Length of stay in hospitals are mostly characterized as asymmetric, right skewed and leptokurtic in nature. Earlier studies have considered parametric distributions like gamma, Pareto, lognormal for studying length of stay of patients in hospitals. However, in this study we have proposed transformed distributions to be the best choice for characterizing the length of stay. For this study, we have considered paediatric asthma dataset and identified that transformed Weibull-Pareto as the best fit. For a comparative purpose we have also provided the results of gamma, lognormal, and Pareto distribution. Maximum likelihood approach is considered to estimate the unknown parameters of the Transformed distribution followed by goodness of fit tests to examine the suitability of the fitted distributions. The results provide a direction for modelling the length of stay in hospitals due to different medical problems which require hospitalization.
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来源期刊
CiteScore
1.40
自引率
14.30%
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