title: Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.
abstract: BACKGROUND
  User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews.
  
  
  METHODS
  We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure.
  
  
  RESULTS
  In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier.
  
  
  CONCLUSIONS
  By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.
authors.affiliation: 1. Antai College of Economics and Management, Shanghai Jiao Tong University , Shanghai, China.
authors.forename: Jingfang
authors.initials: J
authors.lastname: Liu
authors.affiliation: 1. Antai College of Economics and Management, Shanghai Jiao Tong University , Shanghai, China.
authors.forename: Pengzhu
authors.initials: P
authors.lastname: Zhang
authors.affiliation: 2. School of Economics and Management, Beijing University of Chemical Technology , Beijing, China.
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Lu
keywords: Adverse drug reaction; Feature-based classification; Online user review