Unsupervised dynamic topic model for extracting adverse drug reaction from health forums

• Autorzy: Eslami Behnaz , Motlagh Mehdi Habibzadeh , Rezaei Zahra , Eslami Mohammad , Amini Mohammad Amin

Identyfikatory

DOI

10.23743/acs-2020-04

• Języki publikacji: EN

Abstrakty

EN

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 distinct detection of adverse effects of drugs, and deep learning would facilitate it.

Słowa kluczowe

EN

deep learning; topic modeling; Text Mining; ADR; NMF

PL

analiza tekstu; uczenie maszynowe; modelowanie tematyczne; ADR; NMF

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2020
• Tom: Vol. 16, no 1
• Strony: 41--59
• Opis fizyczny: Bibliogr. 19 poz., fig., tab.

Twórcy

autor

Eslami Behnaz

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

autor

Motlagh Mehdi Habibzadeh

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

autor

Rezaei Zahra

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

autor

Eslami Mohammad

• Islamic Azad University of Qazvin,Department of Computer Engineering, Qazvin, Iran

autor

Amini Mohammad Amin

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

Bibliografia

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