title: Using natural language processing to extract mammographic findings.
abstract: OBJECTIVE
  Structured data on mammographic findings are difficult to obtain without manual review. We developed and evaluated a rule-based natural language processing (NLP) system to extract mammographic findings from free-text mammography reports.
  
  
  METHODS
  The NLP system extracted four mammographic findings: mass, calcification, asymmetry, and architectural distortion, using a dictionary look-up method on 93,705 mammography reports from Group Health. Status annotations and anatomical location annotation were associated to each NLP detected finding through association rules. After excluding negated, uncertain, and historical findings, affirmative mentions of detected findings were summarized. Confidence flags were developed to denote reports with highly confident NLP results and reports with possible NLP errors. A random sample of 100 reports was manually abstracted to evaluate the accuracy of the system.
  
  
  RESULTS
  The NLP system correctly coded 96-99 out of our sample of 100 reports depending on findings. Measures of sensitivity, specificity and negative predictive values exceeded 0.92 for all findings. Positive predictive values were relatively low for some findings due to their low prevalence.
  
  
  CONCLUSIONS
  Our NLP system was implemented entirely in SAS Base, which makes it portable and easy to implement. It performed reasonably well with multiple applications, such as using confidence flags as a filter to improve the efficiency of manual review. Refinements of library and association rules, and testing on more diverse samples may further improve its performance.
  
  
  CONCLUSIONS
  Our NLP system successfully extracts clinically useful information from mammography reports. Moreover, SAS is a feasible platform for implementing NLP algorithms.
authors: Group Health Research Institute, Seattle, WA, USA. Electronic address: Gao.h@ghc.org.
authors: Hongyuan
authors: H
authors: Gao
authors: Group Health Research Institute, Seattle, WA, USA.
authors: Erin J
authors: EJ
authors: Aiello Bowles
authors: Group Health Research Institute, Seattle, WA, USA.
authors: David
authors: D
authors: Carrell
authors: Group Health Research Institute, Seattle, WA, USA.
authors: Diana S M
authors: DS
authors: Buist
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D009323:Natural Language Processing; D012680:Sensitivity and Specificity; D012984:Software
keywords: Evaluation; Mammographic findings; Natural language processing; SAS-based