Unsupervised clustering of over-the-counter healthcare products into product categories.
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.
Wallstrom
Garrick L
GL
Department of Biomedical Informatics, University of Pittsburgh, Suite M-183 Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA. garrick@cbmi.pitt.edu
Hogan
William R
WR
D001185:Artificial Intelligence; D016000:Cluster Analysis; D003139:Common Cold / Q000188:drug therapy* / Q000453:epidemiology; D003371:Cough / Q000188:drug therapy* / Q000453:epidemiology; D004196:Disease Outbreaks / Q000517:prevention & control; D015438:Health Behavior; D006801:Humans; D007251:Influenza, Human / Q000188:drug therapy* / Q000453:epidemiology; D004366:Nonprescription Drugs / Q000145:classification* / Q000627:therapeutic use*; D011159:Population Surveillance / Q000379:methods*
D004366:Nonprescription Drugs