title: Detection of pneumonia using free-text radiology reports in the BioSense system.
abstract: OBJECTIVE
  Near real-time disease detection using electronic data sources is a public health priority. Detecting pneumonia is particularly important because it is the manifesting disease of several bioterrorism agents as well as a complication of influenza, including avian and novel H1N1 strains. Text radiology reports are available earlier than physician diagnoses and so could be integral to rapid detection of pneumonia. We performed a pilot study to determine which keywords present in text radiology reports are most highly associated with pneumonia diagnosis.
  
  
  METHODS
  Electronic radiology text reports from 11 hospitals from February 1, 2006 through December 31, 2007 were used. We created a computerized algorithm that searched for selected keywords ("airspace disease", "consolidation", "density", "infiltrate", "opacity", and "pneumonia"), differentiated between clinical history and radiographic findings, and accounted for negations and double negations; this algorithm was tested on a sample of 350 radiology reports. We used the algorithm to study 189,246 chest radiographs, searching for the keywords and determining their association with a final International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis of pneumonia.
  
  
  METHODS
  Performance of the search algorithm in finding keywords, and association of the keywords with a pneumonia diagnosis.
  
  
  RESULTS
  In the sample of 350 radiographs, the search algorithm was highly successful in identifying the selected keywords (sensitivity 98.5%, specificity 100%). Analysis of the 189,246 radiographs showed that the keyword "pneumonia" was the strongest predictor of an ICD-9-CM diagnosis of pneumonia (adjusted odds ratio 11.8) while "density" was the weakest (adjusted odds ratio 1.5). In general, the most highly associated keyword present in the report, regardless of whether a less highly associated keyword was also present, was the best predictor of a diagnosis of pneumonia.
  
  
  CONCLUSIONS
  Empirical methods may assist in finding radiology report keywords that are most highly predictive of a pneumonia diagnosis.
authors.affiliation: Science Applications International Corporation, USA. armen.asatryan@abbott.com
authors.forename: Armenak
authors.initials: A
authors.lastname: Asatryan
authors.forename: Stephen
authors.initials: S
authors.lastname: Benoit
authors.forename: Haobo
authors.initials: H
authors.lastname: Ma
authors.forename: Roseanne
authors.initials: R
authors.lastname: English
authors.forename: Peter
authors.initials: P
authors.lastname: Elkin
authors.forename: Jerome
authors.initials: J
authors.lastname: Tokars
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D004196:Disease Outbreaks / Q000517:prevention & control*; D005260:Female; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D011014:Pneumonia / Q000175:diagnosis* / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics; D011873:Radiology Information Systems; D055815:Young Adult