ArticleJournal of biomedical informatics2007
Unsupervised clustering of over-the-counter healthcare products into product categories.
Article in Journal of biomedical informatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Drug sales data analysis for outbreak detection of infectious diseases: a systematic literature review.BMC infectious diseases · 2014Pooled it
- Evaluating multi-purpose syndromic surveillance systems - a complex problem.Online journal of public health informatics · 2021Article
- Equine syndromic surveillance in Colorado using veterinary laboratory testing order data.PloS one · 2019Article
- The spatiotemporal association of non-prescription retail sales with cases during the 2009 influenza pandemic in Great Britain.BMJ open · 2014Article
- Unsupervised clustering of wildlife necropsy data for syndromic surveillance.BMC veterinary research · 2010Article
- Combining free text and structured electronic medical record entries to detect acute respiratory infections.PloS one · 2010Article
- Mining aggregates of over-the-counter products for syndromic surveillance.Pattern recognition letters · 2009Article
- A Practitioner-Driven Research Agenda for Syndromic Surveillance.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.