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

Garrick L. Wallstrom, William R. Hogan

Research output: Contribution to journalArticle

14 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)642-648
Number of pages7
JournalJournal of Biomedical Informatics
Volume40
Issue number6
DOIs
StatePublished - Dec 1 2007

Keywords

  • Biosurveillance
  • Data analysis
  • Markov chain Monte-Carlo
  • Over-the-counter healthcare products
  • Syndromic surveillance

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Fingerprint Dive into the research topics of 'Unsupervised clustering of over-the-counter healthcare products into product categories'. Together they form a unique fingerprint.

  • Cite this