Adverse Drug Reaction Detection in Social Media by Deep Learning Methods

(Pages: 319-324)
Zahra Rezaei, Ph.D, 1,*Hossein Ebrahimpour-Komleh, Ph.D, 1,*Behnaz Eslami, M.Sc, 2Ramyar Chavoshinejad, D.V.M., 3Mehdi Totonchi, Ph.D., 4,5
Department of Computer and Electrical Engineering, University of Kashan, Kashan, Iran
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Mabna Veterinary Lab, Karaj, Alborz, Iran
Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Tehran, Iran
Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Tehran, Iran
Department of Computer and Electrical Engineering, University of Kashan, Kashan, Iran
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Mabna Veterinary Lab, Karaj, Alborz, Iran
Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Tehran, Iran
Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Tehran, Iran
*Corresponding Address: P.O.Box: 8731753153 Department of Computer and Electrical Engineering University of Kashan Kashan Iran Emails:z.rezaei2010@gmail.com,ebrahimpour@kashanu.ac.ir
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Rezaei Z, Ebrahimpour-Komleh H, Eslami B, Chavoshinejad R, Totonchi M. Adverse drug reaction detection in social media by deep learning methods. Cell J. 2020; 22(3): 319-324. doi: 10.22074/cellj.2020.6615.

Abstract

Objective

Health-related studies have been recently at the heart attention of the media. Social media, such as Twitter, has become a valuable online tool to describe the early detection of various adverse drug reactions (ADRs). Different medications have adverse effects on various cells and tissues, sometimes more than one cell population would be adversely affected. These types of side effect are occasionally associated with the direct or indirect influence of prescribed drugs but do not have general unfavorable mutagenic consequences on patients. This study aimed to demonstrate a quick and accurate method to collect and classify information based on the distribution of approved data on Twitter.

Materials and Methods

In this classification method, we selected "ask a patient" dataset and combination of Twitter "Ask a Patient" datasets that comprised of 6,623, 26,934, and 11,623 reviews. We used deep learning methods with the word2vec to classify ADR comments posted by the users and present an architecture by HAN, FastText, and CNN.

Results

Natural language processing (NLP) deep learning is able to address more advanced peculiarity in learning information compared to other types of machine learning. Moreover, the current study highlighted the advantage of incorporating various semantic features, including topics and concepts.

Conclusion

Our approach predicts drug safety with the accuracy of 93% (the combination of Twitter and "Ask a Patient" datasets) in a binary manner. Despite the apparent benefit of various conventional classifiers, deep learning- based text classification methods seem to be precise and influential tools to detect ADR.