Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based
Jingfang Liu1, Pengzhu Zhang1, Yingjie Lu2
11. Antai College of Economics and Management, Shanghai Jiao Tong University , Shanghai, China.
Iranian Journal of Public Health
|June 11, 2015
Summary
This study developed an effective method to automatically identify adverse drug reactions (ADRs) from online user reviews using a combination of feature sets and the SVM classifier, achieving high accuracy and F-measure for post-marketing drug surveillance.
Area of Science:
- Computational linguistics
- Pharmacovigilance
- Machine learning in healthcare
Background:
- User-generated online content offers valuable data for post-marketing drug surveillance.
- Identifying adverse drug reactions (ADRs) from this data is crucial for public health.
Purpose of the Study:
- To develop an effective automated method for identifying ADR-related messages in online user reviews.
- To compare different feature sets and classification techniques for ADR detection.
Main Methods:
- Collected and annotated 3000 user messages from allergy, schizophrenia, and pain management communities.
- Generated N-gram-based and medical domain-specific feature sets.
- Applied Support Vector Machine (SVM), C4.5, and Naïve Bayes classifiers for ADR identification.
Main Results:
- The SVM classifier achieved an accuracy over 0.8, outperforming C4.5 and Naïve Bayes.
- Combined feature sets (N-gram and domain-specific) consistently outperformed single feature sets.
- The highest F-measure of 0.895 was obtained using combined features and the SVM classifier.
Conclusions:
- A combination of feature sets and the SVM classifier provides an effective approach for automated ADR identification from online user reviews.
- This method enhances the utility of user-generated content for drug safety monitoring.
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