Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations
Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations
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DOI：
10.1063/1.3675621
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发表时间：
2012-03-01
期刊：
影响因子：
2.9
通讯作者：
Hripcsak, George
中科院分区：
文献类型：
--
作者：
Albers, D. J.;Hripcsak, George
本文讨论了如何计算和解释的时间延迟互信息（TPENS）的一个复杂的，离散和稀疏测量，可能是非平稳人口的时间序列的未知成分和起源。用于此分析的主要工具是在总体上平均的时间延迟互信息与聚集总体的时间延迟互信息之间的比较（这里，聚集意味着在实施任何统计估计之前将总体结合）。通过使用信息理论工具，一系列实际可实现的计算进行了详细说明，允许的平均和聚合的时间延迟的互信息进行解释。此外，这些计算也可以用来了解人口中存在的同质或异质性的程度。为了证明所提出的方法可以用于几乎任何情况下，该方法被应用和证明的时间序列的葡萄糖测量从两个不同的亚群的个人从哥伦比亚大学医学中心电子健康记录库，揭示了人口的组成以及生理特征的图片。(C)2012年美国物理学会。[doi 10.1063/1.3675621]
This paper addresses how to calculate and interpret the time-delayed mutual information (TDMI) for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here, aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted. Moreover, these calculations can also be used to understand the degree of homo or heterogeneity present in the population. To demonstrate that the proposed methods can be used in nearly any situation, the methods are applied and demonstrated on the time series of glucose measurements from two different subpopulations of individuals from the Columbia University Medical Center electronic health record repository, revealing a picture of the composition of the population as well as physiological features. (C) 2012 American Institute of Physics. [doi:10.1063/1.3675621]