Discovering Multi-Scale Co-Occurrence Patterns of Asthma and Influenza with Oak Ridge Bio-Surveillance Toolkit

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 a...

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Detalles Bibliográficos
Autores principales: Ramanathan, Arvind, Pullum, Laura L., Hobson, Tanner C., Stahl, Christopher G., Steed, Chad A., Quinn, Shannon P., Chennubhotla, Chakra S., Valkova, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Public Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4522606/
https://www.ncbi.nlm.nih.gov/pubmed/26284230
http://dx.doi.org/10.3389/fpubh.2015.00182

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4522606/
https://www.ncbi.nlm.nih.gov/pubmed/26284230
http://dx.doi.org/10.3389/fpubh.2015.00182

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