title: A review of approaches to identifying patient phenotype cohorts using electronic health records.
abstract: OBJECTIVE
  To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype.
  
  
  METHODS
  We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included.
  
  
  RESULTS
  Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients.
  
  
  CONCLUSIONS
  We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems.
  
  
  CONCLUSIONS
  There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
authors.forename: Chaitanya
authors.initials: C
authors.lastname: Shivade
authors.forename: Preethi
authors.initials: P
authors.lastname: Raghavan
authors.forename: Eric
authors.initials: E
authors.lastname: Fosler-Lussier
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Embi
authors.forename: Noemie
authors.initials: N
authors.lastname: Elhadad
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Johnson
authors.forename: Albert M
authors.initials: AM
authors.lastname: Lai
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D003933:Diagnosis; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010641:Phenotype; D013223:Statistics as Topic; D018875:Vocabulary, Controlled
keywords: Cohort Identification; Electronic Health Records; Phenotyping; Review