Lognormal-based mixture models for robust fitting of hospital length of stay distributions
Lognormal-based mixture models for robust fitting of hospital length of stay distributions
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DOI：
10.1016/j.orhc.2019.04.002
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发表时间：
2019-09-01
影响因子：
2.1
通讯作者：
Smith, Paul
中科院分区：
文献类型：
--
作者：
Zhang, Xu;Barnes, Sean;Smith, Paul
了解住院时间分布的结构可以为医院的手术和临床决策提供支持。我们的目标是开发稳健的方法来拟合这些住院时间分布，这些分布通常是不对称的和多峰的，包含大量的异常值。我们定义了几个基于对数正态分布的混合分布，其中一个分量适合大多数观测数据，另一个分量适合异常观测数据。具体地说，我们提出了三种基于对数正态分布的混合分布，一种使用指数分布作为第二分量，一种使用伽马分布，另一种使用对数正态分布。我们使用期望最大化(EM)算法来估计每个混合模型的参数，并通过仿真来验证我们的模型。最后，我们使用从马里兰大学医学院的研究人员及其同事进行的多项研究中收集的真实数据，比较了我们的混合模型与不同分布拟合的适用性。(C)2019爱思唯尔有限公司。保留所有权利。
Understanding the structure of length of stay distributions can support operational and clinical decision making in hospitals. Our objective is to develop robust methods for fitting these length of stay distributions, which are often skewed and multimodal and contain a significant number of outliers. We define several lognormal-based mixture distributions with two components, one to fit the majority of observations and one to fit the abnormal observations. Specifically, we propose three lognormal-based mixture distributions, one that utilizes the exponential distribution as the second component, one that utilizes the gamma distribution, and one that utilizes the lognormal distribution. We estimate the parameters for each mixture model using the expectation-maximization (EM) algorithm, and validate our models using simulation. Finally, we compare the fit of our mixture models against different distributional fits using real data collected from multiple studies conducted by researchers at the University of Maryland School of Medicine and their colleagues. (C) 2019 Elsevier Ltd. All rights reserved.