title: Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis.
abstract: BACKGROUND
  In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time.
  
  
  METHODS
  In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes.
  
  
  RESULTS
  We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently.
  
  
  CONCLUSIONS
  We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA ; IDEAS Center, Veteran Affair Health Care System, Salt Lake City, UT USA.
authors.forename: Danielle L
authors.initials: DL
authors.lastname: Mowery
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA ; IDEAS Center, Veteran Affair Health Care System, Salt Lake City, UT USA.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Chapman
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA.
authors.forename: Mike
authors.initials: M
authors.lastname: Conway
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA ; IDEAS Center, Veteran Affair Health Care System, Salt Lake City, UT USA.
authors.forename: Brett R
authors.initials: BR
authors.lastname: South
authors.affiliation: San Francisco Veteran Affair Health Care System, San Francisco, CA USA.
authors.forename: Erin
authors.initials: E
authors.lastname: Madden
authors.affiliation: San Francisco Veteran Affair Health Care System, San Francisco, CA USA.
authors.forename: Salomeh
authors.initials: S
authors.lastname: Keyhani
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA ; IDEAS Center, Veteran Affair Health Care System, Salt Lake City, UT USA.
authors.forename: Wendy W
authors.initials: WW
authors.lastname: Chapman
mesh_terms: D000465:Algorithms; D016893:Carotid Stenosis / Q000150:complications; D057225:Data Mining; D057286:Electronic Health Records; D006077:Government Agencies; D006801:Humans; D009323:Natural Language Processing; D010641:Phenotype; D012307:Risk Factors; D020521:Stroke / Q000150:complications; D014728:Veterans
keywords: Information extraction; Natural language processing; Phenotype; Stroke