UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS

Behnaz ESLAMI; Mehdi HABIBZADEH MOTLAGH; Zahra REZAEI; Mohammad ESLAMI; Mohammad AMIN AMINI

doi:10.35784/acs-2020-04

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Published: Mar 30, 2020

DOI: https://doi.org/10.35784/acs-2020-04

Issue Vol. 16 No. 1 (2020)

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UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS
Behnaz ESLAMI, Mehdi HABIBZADEH MOTLAGH, Zahra REZAEI, Mohammad ESLAMI, Mohammad AMIN AMINI

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DOI

https://doi.org/10.35784/acs-2020-04

Authors

Behnaz ESLAMI

behnazeslami30@gmail.com

Islamic Azad University, Science and Research Branch, Department of Computer Engineering, Islamic Azad University, Tehran, Iran, Islamic Republic of

Mehdi HABIBZADEH MOTLAGH

behnazeslami30@gmail.com

P/S/L Group, 1801 McGill College Ave, Montreal, Quebec H3A 2N4, Montreal, Canada

Zahra REZAEI

behnazeslami30@gmail.com

University of Kashan, Department of Computer and Electrical Engineering, Isfahan Province, Qotb-e Ravandi Blvd, Kashan, Iran, Islamic Republic of

Mohammad ESLAMI

behnazeslami30@gmail.com

* Islamic Azad University of Qazvin,Department of Computer Engineering, Qazvin, Iran, Islamic Republic of

Mohammad AMIN AMINI

behnazeslami30@gmail.com

Islamic Azad University of Jasb, Department of Computer Engineering, Markazi, Iran, Islamic Republic of

Abstract

The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: "Ask a patient" website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users' comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users' comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs' side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of dis¬tinct detection of adverse effects of drugs, and deep learning would facilitate it.

Keywords:

Deep Learning, topic modeling, Text Mining, ADR, NMF

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ESLAMI, B., HABIBZADEH MOTLAGH, M., REZAEI, Z., ESLAMI, M., & AMIN AMINI, M. (2020). UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS. Applied Computer Science, 16(1), 41–59. https://doi.org/10.35784/acs-2020-04

UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS

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