A statistical dynamics approach to the study of human health data: Resolving population scale diurnal variation in laboratory data
Open accessAbstract
Statistical physics and information theory is applied to the clinical chemistry measurements present in a patient database containing 2.5 million patients' data over a 20-year period. Despite the seemingly naive approach of aggregating all patients over all times (with respect to particular clinical chemistry measurements), both a diurnal signal in the decay of the time-delayed mutual information and the presence of two sub-populations with differing health are detected. This provides a proof in principle that the highly fragmented data in electronic health records has potential for being useful in defining disease and human phenotypes.
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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 [42]
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References 13 · Cited by 36
Research works printed but not linked: 9
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- Authors
- Albers, D. J., & Hripcsak, G.
- Affiliations
- Columbia University (US)
- Corresponding author
- Albers, D.J.
- Venue
- Physics Letters A
- Publisher
- Elsevier BV
- Published
- 2010-02
- Volume
- 374
- Issue
- 9
- Pages
- 1159-1164
- ISSN
- 0375-9601 (ISSN-L)
- PMC
- PMC2882798
- OpenAlex
- W1984678132
- Cited by
- 36
- References
- 13
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 4 · 2020: 2 · 2019: 1 · 2018: 5 · 2017: 2 · 2016: 1 · 2015: 2 · 2014: 3 · 2013: 2 · 2012: 3
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Physics Letters A (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. National Library of Medicine (R01LM006910)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
- Keywords
- diurnal variation, electronic health records, disease phenotypes, statistical physics, information theory, time series aggregation
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