Discovering multi-scale co-occurrence patterns of asthma and influenza with the Oak Ridge bio-surveillance toolkit
Abstract
Here, we describe a data-driven unsupervised machine learning approach to extract geo-temporal co-occurrence patterns of asthma and the flu from large-scale electronic healthcare reimbursement claims (eHRC) datasets. Specifically, we examine the eHRC data from 2009 to 2010 pandemic H1N1 influenza season and analyze whether different geographic regions within the United States (US) showed an increase in co-occurrence patterns of the flu and asthma. Our analyses reveal that the temporal patterns extracted from the eHRC data show a distinct lag time between the peak incidence of the asthma and the flu. While the increased occurrence of asthma contributed to increased flu incidence during the pandemic, this co-occurrence is predominant for female patients. The geo-temporal patterns reveal that the co-occurrence of the flu and asthma are typically concentrated within the south-east US. Further, in agreement with previous studies, large urban areas (such as New York, Miami, and Los Angeles) exhibit co-occurrence patterns that suggest a peak incidence of asthma and flu significantly early in the spring and winter seasons. Together, our data-analytic approach, integrated within the Oak Ridge Bio-surveillance Toolkit platform, demonstrates how eHRC data can provide novel insights into co-occurring disease patterns.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- IMS Government Solutions (IMSGS), Inc., Fairfax, VA (United States)
- Univ. of Pittsburgh, Pittsburgh, PA (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1286929
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Public Health
- Additional Journal Information:
- Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2296-2565
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; disease co-occurrence; non-negative matrix factorization; public health surveillance; asthma; flu; electronic healthcare reimbursement claims
Citation Formats
Ramanathan, Arvind, Pullum, Laura L., Hobson, Tanner C., Stahl, Christopher G., Steed, Chad A., Valkova, Silvia, Quinn, Shannon, and Chennubhotla, Chakra. Discovering multi-scale co-occurrence patterns of asthma and influenza with the Oak Ridge bio-surveillance toolkit. United States: N. p., 2015.
Web. doi:10.3389/fpubh.2015.00182.
Ramanathan, Arvind, Pullum, Laura L., Hobson, Tanner C., Stahl, Christopher G., Steed, Chad A., Valkova, Silvia, Quinn, Shannon, & Chennubhotla, Chakra. Discovering multi-scale co-occurrence patterns of asthma and influenza with the Oak Ridge bio-surveillance toolkit. United States. https://doi.org/10.3389/fpubh.2015.00182
Ramanathan, Arvind, Pullum, Laura L., Hobson, Tanner C., Stahl, Christopher G., Steed, Chad A., Valkova, Silvia, Quinn, Shannon, and Chennubhotla, Chakra. Mon .
"Discovering multi-scale co-occurrence patterns of asthma and influenza with the Oak Ridge bio-surveillance toolkit". United States. https://doi.org/10.3389/fpubh.2015.00182. https://www.osti.gov/servlets/purl/1286929.
@article{osti_1286929,
title = {Discovering multi-scale co-occurrence patterns of asthma and influenza with the Oak Ridge bio-surveillance toolkit},
author = {Ramanathan, Arvind and Pullum, Laura L. and Hobson, Tanner C. and Stahl, Christopher G. and Steed, Chad A. and Valkova, Silvia and Quinn, Shannon and Chennubhotla, Chakra},
abstractNote = {Here, we describe a data-driven unsupervised machine learning approach to extract geo-temporal co-occurrence patterns of asthma and the flu from large-scale electronic healthcare reimbursement claims (eHRC) datasets. Specifically, we examine the eHRC data from 2009 to 2010 pandemic H1N1 influenza season and analyze whether different geographic regions within the United States (US) showed an increase in co-occurrence patterns of the flu and asthma. Our analyses reveal that the temporal patterns extracted from the eHRC data show a distinct lag time between the peak incidence of the asthma and the flu. While the increased occurrence of asthma contributed to increased flu incidence during the pandemic, this co-occurrence is predominant for female patients. The geo-temporal patterns reveal that the co-occurrence of the flu and asthma are typically concentrated within the south-east US. Further, in agreement with previous studies, large urban areas (such as New York, Miami, and Los Angeles) exhibit co-occurrence patterns that suggest a peak incidence of asthma and flu significantly early in the spring and winter seasons. Together, our data-analytic approach, integrated within the Oak Ridge Bio-surveillance Toolkit platform, demonstrates how eHRC data can provide novel insights into co-occurring disease patterns.},
doi = {10.3389/fpubh.2015.00182},
journal = {Frontiers in Public Health},
number = 1,
volume = 3,
place = {United States},
year = {Mon Aug 03 00:00:00 EDT 2015},
month = {Mon Aug 03 00:00:00 EDT 2015}
}
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