Bi-LSTM based detection of ADR posts using Korean SNS data (Preprint)

BACKGROUND Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been employed, which have studied multiple languages in addition to English. OBJECTIVE A cautionary drug that can cause ADRs in elderly patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a Recurrent Neural Network (RNN). METHODS In previous studies, ketoprofen, which has a high prescription frequency and thus, was selected as the target drug. Posts were collected from portal site containing information about the drug, and NLP techniques for data written in Korean were applied. Posts containing relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. So, a Bi-LSTM classification model was generated. The entire process was further verified using aceclofenac. RESULTS Among the NSAIDs, Korean SNS posts containing Ketoprofen and Aceclofenac information were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post-classification test, ketoprofen and aceclofenac achieved 87% and 80% accuracy, respectively. CONCLUSIONS Here, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracted posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible.