Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations
Open accessAbstract
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.
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CONCERN EWS's methodological choices, including robust modeling of temporal data patterns and use of nursing assessment and observational data, allowed it to have accurate and robust predictive power and sufficient lead time to alter a patient's clinical trajectory.
“Several methodological choices allowed CONCERN EWS to have accurate and robust predictive power and sufficient lead time to alter a patient’s clinical trajectory as we observed in this trial, including: (1) robust modeling of temporal data patterns40–42 (for example, time of day, day of week and patient hospital day) for health care processes (for example, nurses’ surveillance and medication”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [41]
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Agniel, D., Kohane, I. S., & Weber, G. M. (2018) · BMJ · doi:10.1136/bmj.k1479 · Cited by 391 · see citing works
Mining Electronic Health Records (EHRs): A Survey
Yadav, P., Steinbach, M., Kumar, V., & Simon, G. (2018) · ACM Computing Surveys · doi:10.1145/3127881 · Cited by 299 · see citing works
Identifying and mitigating biases in EHR laboratory tests
Pivovarov, R., Albers, D. J., Sepulveda, J. L., & Elhadad, N. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.03.016 · Cited by 91 · see citing works
Self-citationRossetti, S. C., Dykes, P. C., Knaplund, C., Cho, S., Withall, J., Lowenthal, G., Albers, D., Lee, R. Y., Jia, H., Bakken, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03609-7 · Cited by 54 · see citing works
Self-citationZhou, Y., Shi, J., Stein, R., Liu, X., Baldassano, R. N., Forrest, C. B., Chen, Y., & Huang, J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad066 · Cited by 33 · see citing works
- Authors
- Albers, D. J., & Hripcsak, G.
- Affiliations
- Columbia University (US)
- Venue
- Chaos: An Interdisciplinary Journal of Nonlinear Science
- Publisher
- AIP Publishing
- Published
- 2012-01-24
- Volume
- 22
- Issue
- 1
- Pages
- 013111-013111
- ISSN
- 1054-1500 (ISSN-L)
- 1089-7682
- PMC
- PMC3277606
- OpenAlex
- W2067504279
- Cited by
- 53
- References
- 0
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 8 · 2023: 2 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 6 · 2017: 3 · 2016: 3 · 2015: 4 · 2014: 5 · 2013: 2 · 2012: 2
- FWCI
- 9.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Chaos An Interdisciplinary Journal of Nonlinear Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. National Library of Medicine (R01:LM06910, R01LM006910)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- population heterogeneity, demographic structure, time series aggregation, non-stationary time series, sparse observations, electronic health records, information theory
- MeSH terms
- Computer Simulation, Humans, Models, Biological, Population Dynamics, Models, Statistical, Information Storage and Retrieval
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