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Detecting contaminated birthdates using generalized additive models
BACKGROUND: Erroneous patient birthdates are common in health databases. Detection of these errors usually involves manual verification, which can be resource intensive and impractical. By identifying a frequent manifestation of birthdate errors, this paper presents a principled and statistically dr...
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| Hoofdauteurs: | , , , , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
BioMed Central
2014
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4065390/ https://ncbi.nlm.nih.gov/pubmed/24923281 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-15-185 |
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