title: Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases.
abstract: OBJECTIVE
  Most electronic health record databases contain unstructured free-text narratives, which cannot be easily analyzed. Case-detection algorithms are usually created manually and often rely only on using coded information such as International Classification of Diseases version 9 codes. We applied a machine-learning approach to generate and evaluate an automated case-detection algorithm that uses both free-text and coded information to identify asthma cases.
  
  
  METHODS
  The Integrated Primary Care Information (IPCI) database was searched for potential asthma patients aged 5-18 years using a broad query on asthma-related codes, drugs, and free text. A training set of 5032 patients was created by manually annotating the potential patients as definite, probable, or doubtful asthma cases or non-asthma cases. The rule-learning program RIPPER was then used to generate algorithms to distinguish cases from non-cases. An over-sampling method was used to balance the performance of the automated algorithm to meet our study requirements. Performance of the automated algorithm was evaluated against the manually annotated set.
  
  
  RESULTS
  The selected algorithm yielded a positive predictive value (PPV) of 0.66, sensitivity of 0.98, and specificity of 0.95 when identifying only definite asthma cases; a PPV of 0.82, sensitivity of 0.96, and specificity of 0.90 when identifying both definite and probable asthma cases; and a PPV of 0.57, sensitivity of 0.95, and specificity of 0.67 for the scenario identifying definite, probable, and doubtful asthma cases.
  
  
  CONCLUSIONS
  The automated algorithm shows good performance in detecting cases of asthma utilizing both free-text and coded data. This algorithm will facilitate large-scale studies of asthma in the IPCI database.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. m.afzal@erasmusmc.nl
authors.forename: Zubair
authors.initials: Z
authors.lastname: Afzal
authors.forename: Marjolein
authors.initials: M
authors.lastname: Engelkes
authors.forename: Katia M C
authors.initials: KM
authors.lastname: Verhamme
authors.forename: Hettie M
authors.initials: HM
authors.lastname: Janssens
authors.forename: Miriam C J M
authors.initials: MC
authors.lastname: Sturkenboom
authors.forename: Jan A
authors.initials: JA
authors.lastname: Kors
authors.forename: Martijn J
authors.initials: MJ
authors.lastname: Schuemie
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001249:Asthma / Q000453:epidemiology*; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D001330:Electronic Data Processing; D057286:Electronic Health Records; D006801:Humans; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity
keywords: automated case definition; case-detection algorithms; electronic medical records; machine learning; pharmacoepidemiology