Paper Info
Title
Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations.
Abstract
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]
Year
DOI
Venue
2011
10.1063/1.3675621
CHAOS
Keywords
Field
DocType
computer simulation,mutual information,information theory,time series,dynamic system,population dynamics
Population,Data mining,Computer science,Multivariate mutual information,Correlation,Mutual information,Pointwise mutual information
Journal
Volume
Issue
ISSN
22
1.0
1054-1500
Citations 
PageRank 
References 
11
0.70
3
Authors
2
Name
Order
Citations
PageRank
David J Albers116116.04
George Hripcsak21493160.86