Unsupervised clustering of over-the-counter healthcare products into product categories

Authors:
Garrick L. Wallstrom;William R. Hogan
Affiliations:
Department of Biomedical Informatics, University of Pittsburgh, Suite M-183 Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA;Department of Biomedical Informatics, University of Pittsburgh, Suite M-183 Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA
Venue:
Journal of Biomedical Informatics
Year:
2007

Citing 1
Cited 2

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Journal of Biomedical Informatics
Mining aggregates of over-the-counter products for syndromic surveillance

Pattern Recognition Letters

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Abstract

A general problem in biosurveillance is finding appropriate aggregates of elemental data to monitor for the detection of disease outbreaks. We developed an unsupervised clustering algorithm for aggregating over-the-counter healthcare (OTC) products into categories. This algorithm employs MCMC over hundreds of parameters in a Bayesian model to place products into clusters. Despite the high dimensionality, it still performs fast on hundreds of time series. The procedure was able to uncover a clinically significant distinction between OTC products intended for the treatment of allergy and OTC products intended for the treatment of cough, cold, and influenza symptoms.