title: Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection.
abstract: OBJECTIVE
  The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.
  
  
  METHODS
  We selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N(pos)=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations.
  
  
  METHODS
  Classifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed.
  
  
  RESULTS
  Rule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).
  
  
  CONCLUSIONS
  Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.
authors.affiliation: Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, Maryland 20852, USA. taxiarchis.botsis@fda.hhs.gov
authors.forename: Taxiarchis
authors.initials: T
authors.lastname: Botsis
authors.forename: Michael D
authors.initials: MD
authors.lastname: Nguyen
authors.forename: Emily Jane
authors.initials: EJ
authors.lastname: Woo
authors.forename: Marianthi
authors.initials: M
authors.lastname: Markatou
authors.forename: Robert
authors.initials: R
authors.lastname: Ball
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000145:classification; D000707:Anaphylaxis / Q000453:epidemiology* / Q000209:etiology; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D009323:Natural Language Processing; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014481:United States / Q000453:epidemiology; D014765:Viral Vaccines / Q000009:adverse effects*
chemical_list: D014765:Viral Vaccines