UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS
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 e...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2020-03-01
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| Series: | Applied Computer Science |
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| Online Access: | http://acs.pollub.pl/pdf/v16n1/4.pdf |
| _version_ | 1819073149406281728 |
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| author | Behnaz ESLAMI Mehdi HABIBZADEH MOTLAGH Zahra REZAEI Mohammad ESLAMI Mohammad AMIN AMINI |
| author_facet | Behnaz ESLAMI Mehdi HABIBZADEH MOTLAGH Zahra REZAEI Mohammad ESLAMI Mohammad AMIN AMINI |
| author_sort | Behnaz ESLAMI |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T17:49:02Z |
| format | Article |
| id | doaj.art-a45964cdd289418d8b6803a091da413d |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-12-21T17:49:02Z |
| publishDate | 2020-03-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-a45964cdd289418d8b6803a091da413d2022-12-21T18:55:25ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772020-03-01161415910.23743/acs-2020-04UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMSBehnaz ESLAMI0Mehdi HABIBZADEH MOTLAGH1Zahra REZAEI2Mohammad ESLAMI3Mohammad AMIN AMINI4Islamic Azad University, Science and Research Branch, Department of Computer Engineering, Islamic Azad University, Tehran, Iran, behnazeslami30@gmail.comP/S/L Group, 1801 McGill College Ave, Montreal, Quebec H3A 2N4, Montreal, CanadaUniversity of Kashan, Department of Computer and Electrical Engineering, Isfahan Province, Qotb-e Ravandi Blvd, Kashan, IranIslamic Azad University of Qazvin,Department of Computer Engineering, Qazvin, IranIslamic Azad University of Jasb, Department of Computer Engineering, Markazi, IranThe 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.http://acs.pollub.pl/pdf/v16n1/4.pdfdeep learningtopic modelingtext miningadrnmf |
| spellingShingle | Behnaz ESLAMI Mehdi HABIBZADEH MOTLAGH Zahra REZAEI Mohammad ESLAMI Mohammad AMIN AMINI UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS Applied Computer Science deep learning topic modeling text mining adr nmf |
| title | UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS |
| title_full | UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS |
| title_fullStr | UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS |
| title_full_unstemmed | UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS |
| title_short | UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS |
| title_sort | unsupervised dynamic topic model for extracting adverse drug reaction from health forums |
| topic | deep learning topic modeling text mining adr nmf |
| url | http://acs.pollub.pl/pdf/v16n1/4.pdf |
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