luo-detectingcontaminated-2014.pdf (822.07 kB)
Detecting contaminated birthdates using generalized additive models
journal contribution
posted on 2014-06-01, 00:00 authored by Wei LuoWei Luo, M Gallagher, B Loveday, S Ballantyne, J P Connor, J WilesErroneous 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.
History
Journal
BMC bioinformaticsVolume
15Issue
1Article number
185Pagination
1 - 9Publisher
BioMed CentralLocation
London, EnglandPublisher DOI
eISSN
1471-2105Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2014, BioMed CentralUsage metrics
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demographic trendsdomain expertseffective approachesfalse negative ratefalse positivefalse positive ratesgeneralized additive modelpositive predictive valuesScience & TechnologyLife Sciences & BiomedicineBiochemical Research MethodsBiotechnology & Applied MicrobiologyMathematical & Computational BiologyBiochemistry & Molecular BiologyQUALITYRATES
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