title: Congestive heart failure information extraction framework for automated treatment performance measures assessment.
abstract: OBJECTIVE
  This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system - CHIEF - developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF.
  
  
  METHODS
  CHIEF is based on the Apache Unstructured Information Management Architecture framework, and uses a combination of rules, dictionaries, and machine learning methods to extract left ventricular function mentions and values, CHF medications, and documented reasons for a patient not receiving these medications.
  
  
  METHODS
  The training and evaluation of CHIEF were based on subsets of a reference standard of various clinical notes from 1083 Veterans Health Administration patients. Domain experts manually annotated these notes to create our reference standard. Metrics used included recall, precision, and the F 1 -measure.
  
  
  RESULTS
  In general, CHIEF extracted CHF medications with high recall (>0.990) and good precision (0.960-0.978). Mentions of Left Ventricular Ejection Fraction were also extracted with high recall (0.978-0.986) and precision (0.986-0.994), and quantitative values of Left Ventricular Ejection Fraction were found with 0.910-0.945 recall and with high precision (0.939-0.976). Reasons for not prescribing CHF medications were more difficult to extract, only reaching fair accuracy with about 0.310-0.400 recall and 0.250-0.320 precision.
  
  
  CONCLUSIONS
  This study demonstrated that applying natural language processing to unlock the rich and detailed clinical information found in clinical narrative text notes makes fast and scalable quality improvement approaches possible, eventually improving management and outpatient treatment of patients suffering from CHF.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.|Salt Lake City VA Healthcare System, Salt Lake City, UT, USA.
authors.forename: Stéphane M
authors.initials: SM
authors.lastname: Meystre
authors.affiliation: Salt Lake City VA Healthcare System, Salt Lake City, UT, USA.|School of Computing, University of Utah, Salt Lake City, UT, USA.
authors.forename: Youngjun
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
authors.forename: Glenn T
authors.initials: GT
authors.lastname: Gobbel
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Matheny
authors.affiliation: Salt Lake City VA Healthcare System, Salt Lake City, UT, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Bray
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.|Salt Lake City VA Healthcare System, Salt Lake City, UT, USA.
authors.forename: Jennifer H
authors.initials: JH
authors.lastname: Garvin
mesh_terms: D002316:Cardiotonic Agents / Q000627:therapeutic use*; D057286:Electronic Health Records; D006333:Heart Failure / Q000188:drug therapy* / Q000503:physiopathology; D006787:Hospitals, Veterans; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D000069550:Machine Learning; D009323:Natural Language Processing; D016277:Ventricular Function, Left
chemical_list: D002316:Cardiotonic Agents
keywords: Heart Failure [C14.280.434]; Left Ventricular Ejection Fraction; Medical Informatics [L01.313.500]; Natural Language Processing (NLP) [MeSH L01.224.050.375.580]; Quality Indicators, Health Care [N04.761.789]