title: Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.
abstract: OBJECTIVE
  Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF.
  
  
  METHODS
  We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown.
  
  
  METHODS
  Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling.
  
  
  RESULTS
  Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932.
  
  
  CONCLUSIONS
  Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY, United States. Electronic address: roybyrd@us.ibm.com.
authors: Roy J
authors: RJ
authors: Byrd
authors: Geisinger Medical Center, Center for Health Research, Danville, PA, United States.
authors: Steven R
authors: SR
authors: Steinhubl
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY, United States.
authors: Jimeng
authors: J
authors: Sun
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY, United States.
authors: Shahram
authors: S
authors: Ebadollahi
authors: Sutter Health, Research, Development, & Dissemination, Concord, CA, United States.
authors: Walter F
authors: WF
authors: Stewart
mesh_terms: D015331:Cohort Studies; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data*; D001330:Electronic Data Processing; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D009323:Natural Language Processing; D011159:Population Surveillance; D011320:Primary Health Care
keywords: Diagnostic criteria; Electronic health records; Heart failure; Natural language processing; Text mining