title: Development and validation of a classification approach for extracting severity automatically from electronic health records.
abstract: BACKGROUND
  Electronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity is independent of the individual patient's state and is relative to other phenotypes. Further, phenotype-level severity does not change based on the individual patient. For example, acne is mild at the phenotype-level and relative to other phenotypes. Therefore, a given patient may have a severe form of acne (this is the patient-level severity), but this does not effect its overall designation as a mild phenotype at the phenotype-level.
  
  
  METHODS
  We present a method for classifying severity at the phenotype-level that uses the Systemized Nomenclature of Medicine - Clinical Terms. Our method is called the Classification Approach for Extracting Severity Automatically from Electronic Health Records (CAESAR). CAESAR combines multiple severity measures - number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term. CAESAR employs a random forest algorithm and these severity measures to discriminate between severe and mild phenotypes.
  
  
  RESULTS
  Using a random forest algorithm and these severity measures as input, CAESAR differentiates between severe and mild phenotypes (sensitivity = 91.67, specificity = 77.78) when compared to a manually evaluated reference standard (k = 0.716).
  
  
  CONCLUSIONS
  CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY USA ; Observational Health Data Sciences and Informatics (OHDSI), Columbia University, 622 West 168th Street, PH-20, New York, NY USA.
authors.forename: Mary Regina
authors.initials: MR
authors.lastname: Boland
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY USA ; Observational Health Data Sciences and Informatics (OHDSI), Columbia University, 622 West 168th Street, PH-20, New York, NY USA ; Department of Systems Biology, Columbia University, New York, NY USA ; Department of Medicine, Columbia University, New York, NY USA.
authors.forename: Nicholas P
authors.initials: NP
authors.lastname: Tatonetti
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY USA ; Observational Health Data Sciences and Informatics (OHDSI), Columbia University, 622 West 168th Street, PH-20, New York, NY USA.
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
keywords: Data mining; Electronic Health Records; Health status indicators; Outcome assessment (Health Care); Phenotype
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