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

Akhtyamova, L., Alexandrov, M., & Cardiff, J. (2017a). Adverse drug extraction in twitter data using convolutional neural network. In, 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) (pp. 88–92). Lyon.
DOI: https://doi.org/10.1109/DEXA.2017.34   Google Scholar

Akhtyamova, L., Ignatov, A., & Cardiff, J. (2017b). A Large-scale CNN ensemble for medication safety analysis. In F. Frasincar, A. Ittoo, L. Nguyen & E. Métais (Eds.) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science (vol. 10260, pp. 247–253). Springer, Cham.
DOI: https://doi.org/10.1007/978-3-319-59569-6_29   Google Scholar

Bordet, R., Gautier, S., Louet, H. L., Dupuis, B., & Caron, J. (2001). Analysis of the direct cost of adverse drug reactions in hospitalised patients. European journal of clinical pharmacology, 56(12), 935–941.
DOI: https://doi.org/10.1007/s002280000260   Google Scholar

Classen, D. C., Pestotnik, S. L., Evans, R. S., Lloyd, J.F., & Burke, J. P. (1997). Adverse drug events in hospitalized patients: excess length of stay, extra costs, and attributable mortality. Jama, 277(4), 301–306.
DOI: https://doi.org/10.1001/jama.277.4.301   Google Scholar

Ginn, R., Pimpalkhute, P., Nikfarjam, A., Patki, A., O’Connor, K., Sarker, A., Smith, K., & Gonzalez, G. (2014). Mining Twitter for adverse drug reaction mentions, a corpus and classification benchmark. In Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing (pp. 1–8).
  Google Scholar

Gupta, S., Pawar, S., Ramrakhiyani, N., Palshikar, G. K., & Varma, V. (2018). Semi-supervised recurrent neural network for adverse drug reaction mention extraction. BMC bioinformatics, 19(8), 212.
DOI: https://doi.org/10.1186/s12859-018-2192-4   Google Scholar

Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., Jung, K., LePendu, P., & Shah, N. H. (2014). Text mining for adverse drug events, the promise, challenges, and state of the art. Drug safety, 37(10), 777–790.
DOI: https://doi.org/10.1007/s40264-014-0218-z   Google Scholar

Ho, T. B., Le, L., Thai, D. T., & Taewijit, S. (2016). Data-driven approach to detect and predict adverse drug reactions. Current pharmaceutical design, 22(23), 3498–3526.
DOI: https://doi.org/10.2174/1381612822666160509125047   Google Scholar

Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (pp. 427–431). Association for Computational Linguistics.
DOI: https://doi.org/10.18653/v1/E17-2068   Google Scholar

Kongkaew, C., Noyce, P. R., & Ashcroft, D.M. (2008). Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies. Annals of Pharmacotherapy, 42(7–8), 1017–1025.
DOI: https://doi.org/10.1345/aph.1L037   Google Scholar

Lee, K., Qadir, A., Hasan, S. A., Datla, V., Prakash, A., Liu, J., & Farri, O. (2017). Adverse drug event detection in tweets with semi-supervised convolutional neural networks. In Proceedings of the 26th International Conference on World Wide Web (pp. 705–714). Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee. http://doi.org/10.1145/3038912.3052671.
DOI: https://doi.org/10.1145/3038912.3052671   Google Scholar

Miranda, D. S. (2018). Automated detection of adverse drug reactions in the biomedical literature using convolutional neural networks and biomedical word embeddings. SwissText.
  Google Scholar

Rezaei, Z., Ebrahimpour-Komleh, H., Eslami, B., Chavoshinejad, R., & Totonchi, M. (2020). Adverse Drug Reaction Detection in Social Media by Deepm Learning Methods. Cell journal, 22(3), 319–324.
  Google Scholar

Rison, R. A. (2013). A guide to writing case reports. Journal of Medical Case Reports and BioMed Central Research Notes, 7, 239. http://doi.org/10.1186/1752-1947-7-239
DOI: https://doi.org/10.1186/1752-1947-7-239   Google Scholar

Sarker, A., & Gonzalez, G. (2015). Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of biomedical informatics, 53, 196–207.
DOI: https://doi.org/10.1016/j.jbi.2014.11.002   Google Scholar

Sultana, J., Cutroneo, P., & Trifirò, G. (2013). Clinical and economic burden of adverse drug reactions. Journal of pharmacology, 4(Suppl1), 73.
DOI: https://doi.org/10.4103/0976-500X.120957   Google Scholar

Tan, Y., Hu, Y., Liu, X., Yin, Z., wen Chen, X., & Liu, M. (2016). Improving drug safety, From adverse drug reaction knowledge discovery to clinical implementation. Methods, 110, 14–25.
DOI: https://doi.org/10.1016/j.ymeth.2016.07.023   Google Scholar

Vallano, A., Cereza, G., Pedròs, C., Agustí, A., Danés, I., Aguilera, C., & Arnau, J. M. (2005). Obstacles and solutions for spontaneous reporting of adverse drug reactions in the hospital. British journal of clinical pharmacology, 60(6), 653–658.
DOI: https://doi.org/10.1111/j.1365-2125.2005.02504.x   Google Scholar

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, Human Language Technologies (pp. 1480–1489). Association for Computational Linguistics.
DOI: https://doi.org/10.18653/v1/N16-1174   Google Scholar

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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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