Detecting contaminated birthdates using generalized additive models.
| Data(s) |
01/06/2014
|
|---|---|
| Resumo |
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 driven procedure to identify erroneous patient birthdates. |
| Identificador | |
| Idioma(s) |
eng |
| Publicador |
BioMed Central |
| Relação |
http://dro.deakin.edu.au/eserv/DU:30067655/luo-detectingcontaminated-2014.pdf http://www.dx.doi.org/10.1186/1471-2105-15-185 http://www.ncbi.nlm.nih.gov/pubmed/24923281 |
| Direitos |
2014, BioMed Central |
| Palavras-Chave | #demographic trends #domain experts #effective approaches #false negative rate #false positive #false positive rates #generalized additive model #positive predictive values |
| Tipo |
Journal Article |
