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

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


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

Zahra REZAEI


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

Mohammad ESLAMI


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

Mohammad AMIN AMINI


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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Published
2020-03-30

Cited by

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

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

Authors

Mehdi HABIBZADEH MOTLAGH 

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

Authors

Zahra REZAEI 

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

Authors

Mohammad ESLAMI 

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

Authors

Mohammad AMIN AMINI 

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

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