title: Design patterns for the development of electronic health record-driven phenotype extraction algorithms.
abstract: BACKGROUND
  Design patterns, in the context of software development and ontologies, provide generalized approaches and guidance to solving commonly occurring problems, or addressing common situations typically informed by intuition, heuristics and experience. While the biomedical literature contains broad coverage of specific phenotype algorithm implementations, no work to date has attempted to generalize common approaches into design patterns, which may then be distributed to the informatics community to efficiently develop more accurate phenotype algorithms.
  
  
  METHODS
  Using phenotyping algorithms stored in the Phenotype KnowledgeBase (PheKB), we conducted an independent iterative review to identify recurrent elements within the algorithm definitions. We extracted and generalized recurrent elements in these algorithms into candidate patterns. The authors then assessed the candidate patterns for validity by group consensus, and annotated them with attributes.
  
  
  RESULTS
  A total of 24 electronic Medical Records and Genomics (eMERGE) phenotypes available in PheKB as of 1/25/2013 were downloaded and reviewed. From these, a total of 21 phenotyping patterns were identified, which are available as an online data supplement.
  
  
  CONCLUSIONS
  Repeatable patterns within phenotyping algorithms exist, and when codified and cataloged may help to educate both experienced and novice algorithm developers. The dissemination and application of these patterns has the potential to decrease the time to develop algorithms, while improving portability and accuracy.
authors: Feinberg School of Medicine, Northwestern University, Chicago, IL, United States. Electronic address: luke.rasmussen@northwestern.edu.
authors: Luke V
authors: LV
authors: Rasmussen
authors: Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Center for Biomedical Research Informatics, NorthShore University HealthSystem, Evanston, IL, United States.
authors: Will K
authors: WK
authors: Thompson
authors: Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
authors: Jennifer A
authors: JA
authors: Pacheco
authors: Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
authors: Abel N
authors: AN
authors: Kho
authors: Group Health Research Institute, Seattle, WA, United States.
authors: David S
authors: DS
authors: Carrell
authors: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
authors: Jyotishman
authors: J
authors: Pathak
authors: Marshfield Clinic Research Foundation, Marshfield, WI, United States.
authors: Peggy L
authors: PL
authors: Peissig
authors: Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, United States.
authors: Gerard
authors: G
authors: Tromp
authors: Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, United States.
authors: Joshua C
authors: JC
authors: Denny
authors: Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
authors: Justin B
authors: JB
authors: Starren
mesh_terms: D000465:Algorithms; D064229:Biological Ontologies; D066289:Data Curation / Q000379:methods; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification* / Q000458:organization & administration; D023281:Genomics / Q000145:classification* / Q000458:organization & administration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype
keywords: Algorithms; Design patterns; Electronic health record; Phenotype; Software design