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Detecting contaminated birthdates using generalized additive models.

Luo, W, Gallagher, M, Loveday, B, Ballantyne, S, Connor, J P and Wiles, J 2014, Detecting contaminated birthdates using generalized additive models., BMC bioinformatics, vol. 15, no. 1, Article no. 185, pp. 1-9, doi: 10.1186/1471-2105-15-185.

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Title Detecting contaminated birthdates using generalized additive models.
Author(s) Luo, WORCID iD for Luo, W orcid.org/0000-0002-4711-7543
Gallagher, M
Loveday, B
Ballantyne, S
Connor, J P
Wiles, J
Journal name BMC bioinformatics
Volume number 15
Issue number 1
Season Article no. 185
Start page 1
End page 9
Total pages 9
Publisher BioMed Central
Place of publication London, England
Publication date 2014-06
ISSN 1471-2105
Keyword(s) demographic trends
domain experts
effective approaches
false negative rate
false positive
false positive rates
generalized additive model
positive predictive values
Summary 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.
Language eng
DOI 10.1186/1471-2105-15-185
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, BioMed Central
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067655

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.