title: Detecting contaminated birthdates using generalized additive models.
abstract: 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 driven procedure to identify erroneous patient birthdates.
  
  
  RESULTS
  Generalized additive models (GAM) enabled explicit incorporation of known demographic trends and birth patterns. With false positive rates controlled, the method identified birthdate contamination with high accuracy. In the health data set used, of the 58 actual incorrect birthdates manually identified by the domain expert, the GAM-based method identified 51, with 8 false positives (resulting in a positive predictive value of 86.0% (51/59) and a false negative rate of 12.0% (7/58)). These results outperformed linear time-series models.
  
  
  CONCLUSIONS
  The GAM-based method is an effective approach to identify systemic birthdate errors, a common data quality issue in both clinical and administrative databases, with high accuracy.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Geelong, Australia. wei.luo@deakin.edu.au.
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.forename: Marcus
authors.initials: M
authors.lastname: Gallagher
authors.forename: Bill
authors.initials: B
authors.lastname: Loveday
authors.forename: Susan
authors.initials: S
authors.lastname: Ballantyne
authors.forename: Jason P
authors.initials: JP
authors.lastname: Connor
authors.forename: Janet
authors.initials: J
authors.lastname: Wiles
mesh_terms: D063506:Ageism; D016208:Databases, Factual; D005189:False Positive Reactions; D006801:Humans; D008962:Models, Theoretical; D010098:Oxycodone / Q000627:therapeutic use; D010146:Pain / Q000188:drug therapy; D011634:Public Health
chemical_list: D010098:Oxycodone