ArticlePattern recognition letters2009
Mining aggregates of over-the-counter products for syndromic surveillance.
Article in Pattern recognition letters, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drug sales data analysis for outbreak detection of infectious diseases: a systematic literature review.BMC infectious diseases · 2014Pooled it
- Warning about conservation status of forest ecosystems in tropical Andes: National assessment based on IUCN criteria.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
There has recently been a surge of research efforts aimed at very early detection of disease outbreaks. An important strategy for improving the timeliness of outbreak detection is to identify signals that occur early in the epidemic process. We have developed a novel algorithm to identify aggregates of "similar" over-the-counter products that have strong association with a given disease. This paper discusses the proposed algorithm and reports the results of an evaluation experiment. The experimental results show that this algorithm holds promise for discovering product aggregates with outbreak detection performance that is superior to that of predefined categories. We also found that the products extracted by the proposed algorithm were more strongly correlated with the disease data than the standard predefined product categories, while also being more strongly correlated with each other than the products in any predefined category.
Identifiers
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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.