Classifying referring/non-referring ADR in biomedical text using deep learning
An adverse drug reaction (ADR) is a harmful and unwanted disorder that occurs as a result of taking a drug at doses commonly used in humans for the prevention, diagnosis, treatment of disease, or to change physiological function. Reports have shown that about 10% of hospitalizations are due to ADRs....
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823000886 |
| _version_ | 1797800976668688384 |
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| author | Yeganeh Mohammadi Fahimeh Ghasemian Jaleh Varshosaz Mohammad Sattari |
| author_facet | Yeganeh Mohammadi Fahimeh Ghasemian Jaleh Varshosaz Mohammad Sattari |
| author_sort | Yeganeh Mohammadi |
| collection | DOAJ |
| description | An adverse drug reaction (ADR) is a harmful and unwanted disorder that occurs as a result of taking a drug at doses commonly used in humans for the prevention, diagnosis, treatment of disease, or to change physiological function. Reports have shown that about 10% of hospitalizations are due to ADRs. Reporting ADRs has become an essential part of the monitoring and evaluation activities carried out in hospitals. Still, some harmful effects are not reported by drug manufacturers and are unknown to doctors. These effects increase the number of patient hospitalizations. One of the resources that can help in this matter is the huge medical resources available on the web which contain valuable information. Most of these information sources are in the form of text, which should use natural language processing (NLP) techniques for their automatic analysis. The first step in this analysis is to identify texts related to ADR and separate them from unrelated texts. For separating related texts from the massive volume of irrelevant texts, a supervised learning classifier should be used. The purpose of this paper is to apply deep learning models to detect the side effects of drugs in texts using a binary classifier. Deep learning techniques are the state-of-the-art method for text classification. In this article, three approaches based on Recurrent Neural Network (RNN), Gated Recurrent Unit Neural Network (GRU), and Transformer are used for the ADRs identification. The models were trained and evaluated on three different datasets. The results showed that Transformer with 99.12 accuracy is the best model compared to RNN, and GRU. Also, the applied method performs significantly better than previous works. |
| first_indexed | 2024-03-13T04:43:24Z |
| format | Article |
| id | doaj.art-68212c5c45ca49a58b2132adaa322033 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-03-13T04:43:24Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-68212c5c45ca49a58b2132adaa3220332023-06-19T04:29:00ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0139101246Classifying referring/non-referring ADR in biomedical text using deep learningYeganeh Mohammadi0Fahimeh Ghasemian1Jaleh Varshosaz2Mohammad Sattari3Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, IranNovel Drug Delivery Systems Research Centre, Department of Pharmaceutics, School of Pharmacy, Isfahan University of Medical Sciences, Isfahan, IranHealth Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Corresponding author.An adverse drug reaction (ADR) is a harmful and unwanted disorder that occurs as a result of taking a drug at doses commonly used in humans for the prevention, diagnosis, treatment of disease, or to change physiological function. Reports have shown that about 10% of hospitalizations are due to ADRs. Reporting ADRs has become an essential part of the monitoring and evaluation activities carried out in hospitals. Still, some harmful effects are not reported by drug manufacturers and are unknown to doctors. These effects increase the number of patient hospitalizations. One of the resources that can help in this matter is the huge medical resources available on the web which contain valuable information. Most of these information sources are in the form of text, which should use natural language processing (NLP) techniques for their automatic analysis. The first step in this analysis is to identify texts related to ADR and separate them from unrelated texts. For separating related texts from the massive volume of irrelevant texts, a supervised learning classifier should be used. The purpose of this paper is to apply deep learning models to detect the side effects of drugs in texts using a binary classifier. Deep learning techniques are the state-of-the-art method for text classification. In this article, three approaches based on Recurrent Neural Network (RNN), Gated Recurrent Unit Neural Network (GRU), and Transformer are used for the ADRs identification. The models were trained and evaluated on three different datasets. The results showed that Transformer with 99.12 accuracy is the best model compared to RNN, and GRU. Also, the applied method performs significantly better than previous works.http://www.sciencedirect.com/science/article/pii/S2352914823000886ADRDeep learningRecurrent neural networkGated recurrent unitNatural language processingClassification |
| spellingShingle | Yeganeh Mohammadi Fahimeh Ghasemian Jaleh Varshosaz Mohammad Sattari Classifying referring/non-referring ADR in biomedical text using deep learning Informatics in Medicine Unlocked ADR Deep learning Recurrent neural network Gated recurrent unit Natural language processing Classification |
| title | Classifying referring/non-referring ADR in biomedical text using deep learning |
| title_full | Classifying referring/non-referring ADR in biomedical text using deep learning |
| title_fullStr | Classifying referring/non-referring ADR in biomedical text using deep learning |
| title_full_unstemmed | Classifying referring/non-referring ADR in biomedical text using deep learning |
| title_short | Classifying referring/non-referring ADR in biomedical text using deep learning |
| title_sort | classifying referring non referring adr in biomedical text using deep learning |
| topic | ADR Deep learning Recurrent neural network Gated recurrent unit Natural language processing Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823000886 |
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