Shanwen Zhang
ABSTRACT
Adverse drug reaction (ADR) is one of the most important public health problems. Social media has encouraged more patients to share their medication experiences and has become a major source of unreported ADRs in the testing industry. However, since a large number of user posts do not mention any ADRs, it is difficult to accurately detect the presence of ADRs in each user post. An ADR extraction (ADRE) method from social media based on cascade feature extraction and meta-learner (CFEML) is proposed, including text pre-processing, word embedding, feature extraction and feature cascade, and classification by meta-learner. The experimental results show that ADRE based on CFEML can achieve good performance in the classification task of confirming the relationship between the "ADR" pairs in the description of ADRs, and the final model achieves accuracy of 85.52%. The results validate that the proposed method can help evaluators quickly obtain all possible ADRs from social media, and can be used to provide more research means for drug safety evaluation studies.
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Index Terms
- Adverse Drug Reaction from Social Media by Cascade Feature Extraction and Meta-Learner
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