title: Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.
abstract: BACKGROUND
  The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.
  
  
  METHODS
  The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard.
  
  
  RESULTS
  The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded.
  
  
  CONCLUSIONS
  We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.
authors.affiliation: Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA. qzeng@dsg.harvard.edu
authors.forename: Qing T
authors.initials: QT
authors.lastname: Zeng
authors.forename: Sergey
authors.initials: S
authors.lastname: Goryachev
authors.forename: Scott
authors.initials: S
authors.lastname: Weiss
authors.forename: Margarita
authors.initials: M
authors.lastname: Sordo
authors.forename: Shawn N
authors.initials: SN
authors.lastname: Murphy
authors.forename: Ross
authors.initials: R
authors.lastname: Lazarus
mesh_terms: D001249:Asthma / Q000150:complications / Q000175:diagnosis*; D015897:Comorbidity; D006801:Humans; D038801:International Classification of Diseases; D016347:Medical Records Systems, Computerized / Q000592:standards*; D009323:Natural Language Processing; D010351:Patient Discharge; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications / Q000175:diagnosis; D012680:Sensitivity and Specificity; D012907:Smoking / Q000453:epidemiology