Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Automatic Control and Computer Sciences
Published in: Automatic Control and Computer Sciences 5/2020

01-09-2020

Drug Adverse Reaction Discovery Based on Attention Mechanism and Fusion of Emotional Information

Authors: Keming Kang, Shengwei Tian, Long Yu

Published in: Automatic Control and Computer Sciences | Issue 5/2020

Login to get access

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper proposes a research method of adverse drug reactions based on attention mechanism and fusion of emotional information, and constructs a neural network model, Attention based Convolutional neural networks and Bi-directional long short-Term Memory (ACB). In order to improve the recognition efficiency of adverse drug reactions and solve the problems of gradient explosion and disappearance, it introduced the attention mechanism and Bi-directional Long Short-Term Memory (BiLSTM) to enhance the reliability of the model, as well as mixed together the emotional information of the users’ medication comments. Compared with the superficial information only relied on users’ medication reviews, this is able to enhance features’ expression way and the accuracy of the adverse drug reaction’s classifications. This experiment dataset was based on the local drugs in Xinjiang. The best performance on a test dataset was with ACB obtaining a precision of 95.12% and a recall of 98.48%, and an F-score of 96.77%. The results showed that the ACB model can significantly improve the recognition and classification performance of adverse drug reactions.
previous article Optimisation of Parallel Distributed Compensation for Real Time Control of Level in Carbonisation Column
next article Poisson Flows with Alternating Intensity and Their Application
Literature
1.
Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., and Gonzalez, G., Towards internet-age pharmacovigilance: Extracting adverse drug reactions from user posts to health-related social networks, Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Uppsala, 2010, pp. 117–125.
2.
Yates, A. and Goharian, N., ADRTrace: Detecting expected and unexpected adverse drug reactions from user reviews on social media sites, ECIR 2013: Advances in Information Retrieval, 2013, pp. 816–819.
3.
Giacomini, K.M., Krauss, R.M., Roden, D.M., et al., When good drugs do bad, Nature, 2007, vol. 446, no. 7139, pp. 975–977.CrossRef
4.
Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., and Gonzalez, G., Towards internet-age pharmacovigilance: Extracting adverse drug reactions from user posts to health-related social networks, Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Uppsala, 2010, pp. 117–125.
5.
Perez, A., Casillas, A., and Gojenola, K., Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics, Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016), Osaka, 2016, pp. 50–59.
6.
Segura-Bedmar, I., De La Pena, S., and Martinez, P., Extracting drug indications and adverse drug reactions from Spanish health social media, in Proceedings of the 2014 Workshop on Biomedical Natural Language Processing (BioNLP 2014), Baltimore, MD, 2014, pp. 98–106.
7.
Segura-Bedmar, I., Revert, R., and Martinez, P., Detecting drugs and adverse events from Spanish health social media streams, Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi) EACL 2014, Gothenburg, 2014, pp. 106–115.
8.
Ryan, R.J., Groundtruth budgeting: A novel approach to semi-supervised relation extraction of medical language, Master’s (Eng.) Thesis, Massachusetts Institute of Technology, 2011.
9.
Alhuzali, H. and Ananiadou, S., Improving classification of adverse drug reactions through using sentiment analysis and transfer learning, Proceedings of the 18th BioNLP Workshop and Shared Task, 2019, pp. 339–347.
10.
Chowdhury, S., Zhang, C., and Yu, P.S., Multi-task pharmacovigilance mining from social media posts, Proceedings of the 2018 World Wide Web Conference—International World Wide Web Conferences Steering Committee, 2018, pp. 117–126.
11.
Korkontzelos, I., et al., Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts, J. Biomed. Inf., 2016, vol. 62, pp. 148–158.CrossRef
12.
Mikolov, T., Sutskever, I., Chen, K., et al., Distributed representations of words and phrases and their compositionality, Proceedings of Advances in Neural Information Processing Systems, 2013, pp. 3111–3119.
13.
Aroyehun, S.T. and Gelbukh, A., Automatic identification of drugs and adverse drug reaction related tweets, Proceedings of the 3rd Social Media Mining for Health Applications (SMM4H) Workshop & Shared Task, Brussels, 2018, pp. 54–55.
14.
Benzschawel, E., Identifying potential adverse drug events in tweets using bootstrapped lexicons, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics—Student Research Workshop, Berlin, 2016, pp. 15–21.
15.
Chuhan Wu, Fangzhao Wu, Junxin Liu, Sixing Wu, Yongfeng Huang, and Xing Xie, Detecting tweets mentioning drug name and adverse drug reaction with hierarchical tweet representation and multi-head self-attention, Proceedings of the 3rd Social Media Mining for Health Applications (SMM4H) Workshop & Shared Task, Brussels, 2018, pp. 34–37.
16.
Henriksson, A., Representing clinical notes for adverse drug event detection, Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis (Louhi), Lisbon, 2015, pp. 152–158.
17.
Jin Wang, Liang-Chih Yu, Lai, K.R., and Xuejie Zhang, Dimensional sentiment analysis using a regional CNN–LSTM model, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 2016, pp. 225–230.
18.
Shin, B., Lee, T., and Cho, J.D., Lexicon integrated CNN models with attention for sentiment analysis, Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Copenhagen, 2017, pp. 149–158.
19.
Tian Sheng-Wei, Qin Yue, Yu Long, Turglm Ibrahim, and Feng Guan-jun, Anaphora resolution of Uyghur personal pronouns based on Bi-LSTM, Acta Electron. Sin., 2018, vol. 46, no. 7, pp. 1691–1699.
20.
Trung, H., He, Yu., Willis, A., and Ruger, S., Adverse drug reaction classification with deep neural networks, Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, 2016, pp. 877–887.
21.
Stanovsky, G., Gruhl, D., and Mendes, P.N., Recognizing mentions of adverse drug reaction in social media using knowledge-infused recurrent models, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, 2017, vol. 1, pp. 142–151.
22.
Santiso, S., Perez, A., Gojenola, K., Casillas, A., and Oronoz, M., Adverse drug event prediction combining shallow analysis and machine learning, Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi) EACL 2014, Gothenburg, 2014, pp. 85–89.
23.
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., and Bizer, C., DB-pedia: A large-scale, multilingual knowledge base extracted from Wikipedia, Semantic Web J., 2015, vol. 6, no. 2, pp. 167–195.CrossRef
24.
Stanovsky, G., Dagan, I., and Mausam, Open IE as an intermediate structure for semantic tasks, in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015, vol. 2, pp. 303–308.
Metadata
Title
Drug Adverse Reaction Discovery Based on Attention Mechanism and Fusion of Emotional Information
Authors
Keming Kang
Shengwei Tian
Long Yu
Publication date
01-09-2020
Publisher
Pleiades Publishing
Published in
Automatic Control and Computer Sciences / Issue 5/2020
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411620050053

Other articles of this Issue 5/2020

Automatic Control and Computer Sciences 5/2020 Go to the issue

OriginalPaper

Optimal Trajectory Planning and Control for Automatic Lane Change of in Wheel Motor Driving Vehicles on Snow and Ice Roads

OriginalPaper

Research on User Behavior Prediction and Profiling Method Based on Trajectory Information

OriginalPaper

Poisson Flows with Alternating Intensity and Their Application

OriginalPaper

Satellite Image Encryption Based on AES and Discretised Chaotic Maps

OriginalPaper

Steganalysis Method of Static JPEG Images Based on Artificial Immune System

OriginalPaper

A Novel Method of SOC Estimation for Electric Vehicle Based on Adaptive Particle Filter