Research and Enlightenment of Text Mining Applications in ADR from Social Media

Abstract

Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications, and to provide new ideas and methods for pharmacovigilance. Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning, text mining based on deep learning (neural networks) and adverse drug reaction (ADR) terminology. Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine (SVM) algorithm, naive Bayesian (NB) classifier, decision tree, hidden Markov model (HMM) and bidirectional en-coder representations from transformers (BERT). The main neural network text mining based on deep learning are convolution neural network (CNN), recurrent neural network (RNN) and long short-term memory (LSTM). ADR terminology standardization tools mainly include “Medical Dictionary for Regulatory Activities” (MedDRA), “WHODrug” and “Systematized Nomenclature of Medicine-Clinical Terms” (SNOMED CT).

Keywords

social media data / text mining / adverse drug reaction

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