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Data mining / Clinical surveillance / Disease surveillance / Geographic information system / Influenza / Statistics / Medicine / Epidemiology / Health / Data analysis


Detecting Anomalous Patterns in Pharmacy Retail Data Maheshkumar R. Sabhnani Daniel B. Neill Andrew W. Moore
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Document Date: 2005-12-13 14:57:06


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City

Pittsburgh / Chicago / /

Company

Neural Information Processing Systems / RODS (Real-time Outbreak and Disease Surveillance) Laboratory / RODS Laboratory / /

Country

United States / /

Currency

USD / /

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Facility

Pharmacy Retail Data Maheshkumar R. Sabhnani Daniel B. Neill Andrew W. Moore School / Computer Science Carnegie Mellon University / University of Pittsburgh / store ID / /

Holiday

national holiday / Memorial Day / /

IndustryTerm

outbreak detection tools / Web Figure / viewer tool / retail data / treatment of infectious diseases / search region / reasonable univariate time series algorithm / similar products / viewer application tool / search regions / pharmacy retail / search algorithm / /

Organization

American Medical Informatics Association / University of Pittsburgh / Pharmacy Retail Data Maheshkumar R. Sabhnani Daniel B. Neill Andrew W. Moore School / Carnegie Mellon University / /

Person

Jeremy U. Espino / Michael M. Wagner / Fu-Chiang Tsui / /

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Position

Data Mining General / /

Product

Internal / /

ProvinceOrState

Alabama / Pennsylvania / Illinois / Georgia / /

PublishedMedium

Morbidity and Mortality Weekly Report / /

Region

eastern United States / /

Technology

series algorithms / search algorithm / reasonable univariate time series algorithm / Data Mining / /

SocialTag