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 language | English (US) |
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Pages (from-to) | 642-648 |
Number of pages | 7 |
Journal | Journal of Biomedical Informatics |
Volume | 40 |
Issue number | 6 |
DOIs | |
State | Published - Dec 2007 |
Externally published | Yes |
Keywords
- Biosurveillance
- Data analysis
- Markov chain Monte-Carlo
- Over-the-counter healthcare products
- Syndromic surveillance
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications