title: Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure.
abstract: OBJECTIVE
  Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics.
  
  
  METHODS
  We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements.
  
  
  RESULTS
  System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%).
  
  
  CONCLUSIONS
  An EF value of <40% can be accurately identified in VA echocardiogram reports.
  
  
  CONCLUSIONS
  An automated information extraction system can be used to accurately extract EF for quality measurement.
authors.affiliation: IDEAS Center, SLC VA Healthcare System, Salt Lake City, Utah 84148, USA. jennifer.garvin@va.gov
authors.forename: Jennifer H
authors.initials: JH
authors.lastname: Garvin
authors.forename: Scott L
authors.initials: SL
authors.lastname: DuVall
authors.forename: Brett R
authors.initials: BR
authors.lastname: South
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Bray
authors.forename: Daniel
authors.initials: D
authors.lastname: Bolton
authors.forename: Julia
authors.initials: J
authors.lastname: Heavirland
authors.forename: Steve
authors.initials: S
authors.lastname: Pickard
authors.forename: Paul
authors.initials: P
authors.lastname: Heidenreich
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.forename: Charlene
authors.initials: C
authors.lastname: Weir
authors.forename: Matthew
authors.initials: M
authors.lastname: Samore
authors.forename: Mary K
authors.initials: MK
authors.lastname: Goldstein
mesh_terms: D057225:Data Mining / Q000379:methods*; D004452:Echocardiography; D006333:Heart Failure / Q000000981:diagnostic imaging / Q000628:therapy; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D019984:Quality Indicators, Health Care; D012015:Reference Standards; D012986:Software Validation; D013318:Stroke Volume; D014481:United States; D014493:United States Department of Veterans Affairs