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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Detalles Bibliográficos
Autores principales: Luo, Wei, Gallagher, Marcus, Loveday, Bill, Ballantyne, Susan, Connor, Jason P, Wiles, Janet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4065390/
https://www.ncbi.nlm.nih.gov/pubmed/24923281
http://dx.doi.org/10.1186/1471-2105-15-185

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4065390/
https://www.ncbi.nlm.nih.gov/pubmed/24923281
http://dx.doi.org/10.1186/1471-2105-15-185

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