Adverse Drug Reaction Detection with Drug Prediction Using BERT–LSTM–SVM

Social media can be a useful resource for assessing patient-reported drug experiences. Adverse drug reactions (ADRs) are an important public health issue, but finding social media posts in which adverse reactions are described is a difficult task, since not all posts are related to ADR. The proposed work in this research is a hybrid model of BERT-LSTM-SVM for detection of ADRs and drug classification. The input is preprocessed as follows: the text is cleaned, tokenized, normalized, and noise is removed. Bidirectional Encoder Representations from Transformers (BERT) is used to produce contextual representations of the text, and a Long Short-Term Memory network (LSTM) is used to model sequential dependencies to aid binary classification of ADR and non-ADR text. Then, a Support Vector Machine (SVM) classifier is used to classify the associated medicine based on the extracted textual features. In our validation of the method, the accuracy, sensitivity, specificity and precision were 0.99 with a recall. The proposed framework combines contextual language representations, sequential modeling, and supervised classification approaches to facilitate the automatic detection of ADRs and medicine identification.