A Review on Adverse Drug Reaction Detection Techniques
Abstract
The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient’s view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the first discusses a survey on detecting ADRs and side effects, followed by an examination of biomedical text mining that focuses on identifying the specific relationships involving ADRs. Finally, we will provide a general overview of sentiment analysis, particularly from a medical perspective. This study presents a survey on ADRs extracted from drug review sentences on social media, utilizing and comparing different techniques.
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Ahmad, S.R., 2003. Adverse drug event monitoring at the Food and Drug Administration. Journal of General Internal Medicine, 18(1), pp.57-60. DOI: https://doi.org/10.1046/j.1525-1497.2003.20130.x
Akhtyamova, L., Alexandrov, M., and Cardiff, J., 2017. Adverse Drug Extraction in Twitter Data using Convolutional Neural Network. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, pp.88-92. DOI: https://doi.org/10.1109/DEXA.2017.34
Ananiadou, S., Pyysalo, S., Tsujii, J., and Kell, D.B., 2010. Event extraction for systems biology by text mining the literature. Trends in Biotechnology, 28(7), pp.381-390. DOI: https://doi.org/10.1016/j.tibtech.2010.04.005
Aronson, A.R., 2001, Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program. In: Proceedings of the AMIA Symposium. American Medical Informatics Association, p.17.
Bates, D.W., Scott Evans, R., Murff, H., Stetson, P.D., Pizziferri, L., and Hripcsak, G., 2003. Detecting adverse events using information technology. Journal of the American Medical Informatics Association, 10(2), pp.115-128. DOI: https://doi.org/10.1197/jamia.M1074
Bijlani, N., Nilforooshan, R., and Kouchaki, S., 2022. An unsupervised data-driven anomaly detection approach for adverse health conditions in people living with dementia: Cohort study. JMIR Aging, 5(3), p.e38211. DOI: https://doi.org/10.2196/38211
Cao, H., Hripcsak, G., and Markatou, M., 2007. A statistical methodology for analyzing co-occurrence data from a large sample. Journal of Biomedical Informatics, 40(3), pp.343-352. DOI: https://doi.org/10.1016/j.jbi.2006.11.003
Cocos, A., Fiks, A.G., and Masino, A.J., 2017. Deep learning for pharmacovigilance: Recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. Journal of the American Medical Informatics Association, 24(4), pp.813-821. DOI: https://doi.org/10.1093/jamia/ocw180
Cohen, A.M., Hersh, W.R., 2005. A survey of current work in biomedical text mining. Briefings in Bioinformatics, 6(1), pp.57-71. DOI: https://doi.org/10.1093/bib/6.1.57
Cohen, K.B., and Hunter, L., 2008. Getting started in text mining. PLoS Computational Biology, 4(1), p.e20. DOI: https://doi.org/10.1371/journal.pcbi.0040020
Dai, H.J., and Wang, C.K., 2019. Classifying adverse drug reactions from imbalanced twitter data. International Journal of Medical Informatics, 129, pp.122-132. DOI: https://doi.org/10.1016/j.ijmedinf.2019.05.017
Davis, J.M., 1965. Efficacy of tranquilizing and antidepressant drugs. Archives of General Psychiatry, 13(6), pp.552-572. DOI: https://doi.org/10.1001/archpsyc.1965.01730060070010
De Rosa, M., Fenza, G., Gallo, A., Gallo, M., and Loia, V., 2021. Pharmacovigilance in the era of social media: discovering adverse drug events cross-relating Twitter and PubMed. Future Generation Computer Systems, 114, pp.394-402. DOI: https://doi.org/10.1016/j.future.2020.08.020
Denecke, K., and Nejdl, W., 2009. How valuable is medical social media data? Content analysis of the medical web. Information Sciences, 179(12), pp.1870-1880. DOI: https://doi.org/10.1016/j.ins.2009.01.025
Ebrahimi, M., Yazdavar, A., Salim, N., and Eltyeb, S., 2016. Recognition of side effects as implicit-opinion words in drug reviews. Online Information Review, 40(7), pp.1018-1032. DOI: https://doi.org/10.1108/OIR-06-2015-0208
Edwards, I.R., and Aronson, J.K., 2000. Adverse drug reactions: Definitions, diagnosis, and management. The Lancet, 356(9237), pp.1255-1259. DOI: https://doi.org/10.1016/S0140-6736(00)02799-9
Emadzadeh, E., Sarker, A., Nikfarjam, A., and Gonzalez, G., 2018. Hybrid semantic analysis for mapping adverse drug reaction mentions in tweets to medical terminology. The AMIA Annual Symposium, 2017, pp.679-688.
Fan, B., Fan, W., and Smith, C., 2020. Adverse drug event detection and extraction from open data: A deep learning approach. Information Processing and Management, 57(1), p.102131. DOI: https://doi.org/10.1016/j.ipm.2019.102131
Ginn, R., Pimpalkhute, P., and Nikfarjam, A., 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. Citeseer, pp.1-8.
Goeuriot, L., Na, J.C., Kyaing, W.Y.M., Foo, S., Khoo, C., Theng, Y.L., and Chang, Y.K., 2011. Textual and informational characteristics of health-related social media content: A study of drug review forums, p. 548-557.
Govindarajan, M., 2013. Sentiment analysis of movie reviews using hybrid method of naive bayes and genetic algorithm. International Journal of Advanced Computer Research, 3(4), p.139.
Grishman, R., and Sundheim, B.M., 1996. Message Understanding Conference-6: ABrief History. In: COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics. DOI: https://doi.org/10.3115/992628.992709
Gurulingappa, H., Mateen-Rajpu, A., and Toldo, L., 2012. Extraction of potential adverse drug events from medical case reports. Journal of Biomedical Semantics. Journal of Biomedical Semantics, 3(1), pp.15. DOI: https://doi.org/10.1186/2041-1480-3-15
Hacker, M., 2009. Adverse drug reactions. In: Pharmacology. Elsevier, Netherlands, pp.327-352. DOI: https://doi.org/10.1016/B978-0-12-369521-5.00013-0
Hanauer, D., 2007. Mining Clinical Electronic Data for Research and Patient Care: Challenges and Solutions. Clinical Assistant Professor University of Michigan, USA.
Harpaz, R., DuMouchel, W., Shah, N.H., Madigan, D., Ryan, P., and Friedman, C., 2012. Novel data-mining methodologies for adverse drug event discovery and analysis. Clinical Pharmacology and Therapeutics, 91(6), pp.1010-1021. DOI: https://doi.org/10.1038/clpt.2012.50
Honigman, B., Lee, J., Rothschild, J., Light, P., Pulling, R.M., Yu, T., Bates, D.W., 2001. Using computerized data to identify adverse drug events in outpatients. Journal of the American Medical Informatics Association, 8(3), pp.254-266. DOI: https://doi.org/10.1136/jamia.2001.0080254
Huang, J., Lu, J., and Ling, C.X., 2003. Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy. In: Third IEEE International Conference on Data Mining. IEEE, pp.553-556.
Joachims, T., 1998. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: European Conference on Machine Learning. Springer, Berlin, pp.137-142. DOI: https://doi.org/10.1007/BFb0026683
Kiritchenko, S., Mohammad, S.M., Morin, J., and de Bruijn, B., 2018. NRC-Canada at SMM4H shared task: Classifying Tweets mentioning adverse drug reactions and medication intake. arXiv preprint arXiv:1805.04558.
Kiritchenko, S., Zhu, X., and Mohammad, S.M., 2014. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, pp.723-762. DOI: https://doi.org/10.1613/jair.4272
Lazarou, J., Pomeranz, B.H., and Corey, P.N., 1998. Incidence of adverse drug reactions in hospitalized patients: Ameta-analysis of prospective studies. JAMA, 279(15), pp.1200-1205. DOI: https://doi.org/10.1001/jama.279.15.1200
Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., and Gonzalez, G., 2010. Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts to Health-Related Social Networks. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, pp.117-125.
Lee, K., Qadir, A., Hasan, S.A., Datla, V., Prakash, A., Liu, J., and Farri, D., 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. DOI: https://doi.org/10.1145/3038912.3052671
Lee, L.H., Wan, C.H., Rajkumar, R., and Isa, D., 2012. An enhanced support vector machine classification framework by using Euclidean distance function for text document categorization. Applied Intelligence, 37(1), pp.80-99. DOI: https://doi.org/10.1007/s10489-011-0314-z
Li, Y.A., 2011. Medical Data Mining: Improving Information Accessibility Using Online Patient Drug Reviews. Massachusetts Institute of Technology, Cambridge.
Li, Z., Yang, Z., Luo, L., Xiang, Y., and Lin, H., 2020. Exploiting adversarial transfer learning for adverse drug reaction detection from texts. Journal of Biomedical Informatics, 106, p.103431. DOI: https://doi.org/10.1016/j.jbi.2020.103431
Liu, Y., Bi, J.W., and Fan, Z.P., 2017. Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, pp. 149-161. DOI: https://doi.org/10.1016/j.inffus.2016.11.012
Moghaddam, S., and Ester, M., 2012. Aspect-Based Opinion Mining from Online Reviews. In: Tutorial at SIGIR Conference. DOI: https://doi.org/10.1145/2348283.2348533
Montgomery, D.C., Peck, E.A., and Vining, G.G., 2021. Introduction to Linear Regression Analysis. John Wiley & Sons, United States.
Nafea, A.A., Ibrahim, M.S., Mukhlif, A.A., AL-Ani, M.M., and Omar, N., 2024. An ensemble model for detection of adverse drug reactions. ARO-The Scientific Journal of Koya University, 12(1), pp.41-47. DOI: https://doi.org/10.14500/aro.11403
Nafea, A.A., Omar, N., and AL-Ani, M.M., 2021. Adverse drug reaction detection using latent semantic analysis. Journal of Computer Science, 17(10), pp.960-970. DOI: https://doi.org/10.3844/jcssp.2021.960.970
Nafea, A.A., Omar, N., and Al-Qfail, Z.M., 2024. Artificial neural network and latent semantic analysis for adverse drug reaction detection. Baghdad Science Journal, 21, pp.226-233. DOI: https://doi.org/10.21123/bsj.2023.7988
Narayanasamy, V., Mukhopadhyay, S., Palakal, M., and Potter, D.A., 2004. TransMiner: Mining transitive associations among biological objects from text. Journal of Biomedical Science, 11(6), pp.864-873. DOI: https://doi.org/10.1159/000081834
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., and Gonzalez, G., 2015. Pharmacovigilance from social media: Mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3), pp.671-681. DOI: https://doi.org/10.1093/jamia/ocu041
Odeh, F., and Taweel, A., 2019. ADeep Learning Approach to Extracting Adverse Drug Reactions. In: 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE, pp.1-6. DOI: https://doi.org/10.1109/AICCSA47632.2019.9035272
Pain, J., Levacher, J., Quinquenel, A., and Belz, A., 2016. Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilanc. In: Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pp.94-101.
Pirmohamed, M., James, S., Meakin, S., Green, C., Scott, A.K., Walley, T.J., Farrar, K., Park, B.K., and Breckenridge, A.M., 2004. Adverse drug reactions as cause of admission to hospital: Prospective analysis of 18 820 patients. BMJ, 329(7456), pp.15-19. DOI: https://doi.org/10.1136/bmj.329.7456.15
Plachouras, V., Leidner, J.L., and Garrow, A.G., 2016. Quantifying Self-Reported Adverse Drug Events on Twitter: Signal and Topic Analysis. In: Proceedings of the 7th 2016 International Conference on Social Media and Society, pp.1-10. DOI: https://doi.org/10.1145/2930971.2930977
Pouliot, Y., Chiang, A.P., and Butte, A.J., 2011. Predicting adverse drug reactions using publicly available PubChem BioAssay data. Clinical Pharmacology and Therapeutics, 90(1), pp.90-99. DOI: https://doi.org/10.1038/clpt.2011.81
Prabowo, R., and Thelwall, M., 2009. Sentiment analysis: Acombined approach. Journal of Informetrics, 3(2), pp.143-157. DOI: https://doi.org/10.1016/j.joi.2009.01.003
Rebholz-Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, M., and Stoehr, P., 2007. EBIMed-text crunching to gather facts for proteins from Medline. Bioinformatics, 23(2), pp.e237-e244. DOI: https://doi.org/10.1093/bioinformatics/btl302
Rindflesch, T.C., and Fiszman, M., 2003. The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics, 36(6), pp.462-477. DOI: https://doi.org/10.1016/j.jbi.2003.11.003
Roddick, J.F., Fule, P., and Graco, W.J., 2003. Exploratory medical knowledge discovery: Experiences and issues. ACM SIGKDD Explorations Newsletter, 5(1), pp.94-99. DOI: https://doi.org/10.1145/959242.959243
Roitmann, E., Eriksson, R., and Brunak, S., 2014. Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events. Frontiers in Physiology, 5, p.332. DOI: https://doi.org/10.3389/fphys.2014.00332
Sangma, J.W., Anal, S.R.N., and Pal, V., 2020. Clustering-based hybrid approach for identifying uantitative multidimensional associations between patient Attributes, drugs and adverse drug reactions. Interdisciplinary Sciences: Computational Life Sciences, 12, pp.237-251. DOI: https://doi.org/10.1007/s12539-020-00365-9
Sarker, A., and Gonzalez, G., 2015. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of Biomedical Informatics, 53, pp.196-207. DOI: https://doi.org/10.1016/j.jbi.2014.11.002
Sarker, A., Ginn, R., Nikfarjam, A., O’Connor, K., Smith, K., Jayaraman, S., Upadhaya, T., and Gonzalez, G., 2015. Utilizing social media data for pharmacovigilance: Areview. Journal of Biomedical Informatics, 54, pp.202-212. DOI: https://doi.org/10.1016/j.jbi.2015.02.004
Shen, C., Li, Z., Chu, Y., and Zhao, Z., 2021. GAR: Graph adversarial representation for adverse drug event detection on Twitter. Applied Soft Computing, 106, p.107324. DOI: https://doi.org/10.1016/j.asoc.2021.107324
Shen, C., Lin, H., Guo, K., Xu, K., Yang, Z., and Wang, J., 2019. Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks. Neural Computing and Applications, 31, pp.4799-4808. DOI: https://doi.org/10.1007/s00521-018-3722-8
Simpson, M.S., and Demner-Fushman, D., 2012. Biomedical text mining: Asurvey of recent progress. In: Mining Text Data. Springer, Berlin, pp.465-517. DOI: https://doi.org/10.1007/978-1-4614-3223-4_14
Skentzos, S., Shubina, M., Plutzky, J., and Turchin, A., 2011. Structured vs. unstructured: Factors affecting adverse drug reaction documentation in an EMR repository. AMIA Annual Symposium Proceedings, 2011, p.1270.
Somasundaran, S., Wilson, T., Wiebe, J., and Stoyanov, V., 2007. QA with Attitude: Exploiting Opinion TypeAnalysis for Improving Question Answering in On-line Discussions and the News. In: The International AAAI Conference on Web and Social Media.
Tan, S., Cheng, X., Wang, Y., and Xu, H., 2009. Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis. In: European Conference on Information Retrieval. Springer, Berlin, pp.337-349. DOI: https://doi.org/10.1007/978-3-642-00958-7_31
Thabtah, F., Eljinini, M.A.H., and Hadi, W.M., 2009. Naïve Bayesian Based on Chi SQUARE to Categorize Arabic Data. In: Proceedings of the 11th International Business Information Management Association Conference (IBIMA) Conference on Innovation and Knowledge Management in Twin Track Economies, Cairo, Egypt, pp.4-6.
Wang, C.K., Dai, H., Su, E.C.Y., and Wang, F.D., 2018. Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques. In: 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, pp.5-9. DOI: https://doi.org/10.1109/TAAI.2018.00011
Wang, C.S., Lin, P.F., Cheng, C.L., Tai, S.H., Yang, Y.H.K., and Chiang, J.H., 2019. Detecting potential adverse drug reactions using a deep neural network model. Journal of Medical Internet Research, 21(2), p.e11016. DOI: https://doi.org/10.2196/11016
Wang, X., Tsujii, J., and Ananiadou, S., 2010. Disambiguating the species of biomedical named entities using natural language parsers. Bioinformatics, 26(5), pp.661-667. DOI: https://doi.org/10.1093/bioinformatics/btq002
Weeber, M., Klein, H., Aronson, A.R., Mork, J.G., de Jong-van den Berg, L.T., and Vos, R., 2000. Text-Based Discovery in Biomedicine: The Architecture of the DAD-System. In: Proceedings of the AMIA Symposium. American Medical Informatics Association, p. 903.
WHO., 2002. The Importance of Pharmacovigilance. WHO, Geneva. Xu, K., Liao, S.S., Li, J., and Song, Y., 2011. Mining comparative opinions from customer reviews for competitive intelligence. Decision Support Systems, 50(4), pp.743-754. DOI: https://doi.org/10.1016/j.dss.2010.08.021
Yadesa, T.M., Kitutu, F.E., Deyno, S., Ogwang, P.E., Tamukong, R., and Alele, P.E., 2021. Prevalence, characteristics and predicting risk factors of adverse drug reactions among hospitalized older adults: A systematic review and meta-analysis. SAGE Open Medicine, 9, p. 20503121211039100. DOI: https://doi.org/10.1177/20503121211039099
Yalamanchi, D., 2011. Sideffective-System to Mine Patient Reviews: Sentiment Analysis. Rutgers University-Graduate School-New Brunswick, New Jersey.
Yates, A., and Goharian, N., 2013. ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites. In: Advances in Information Retrieval: 35th European Conference on IR Research, ECIR 2013, Moscow, Russia. Proceedings 35. Springer Berlin Heidelberg, pp.816-819. DOI: https://doi.org/10.1007/978-3-642-36973-5_92
Yousef, R.N.M., Tiun, S., and Omar, N., 2019. Extended trigger terms for extracting adverse drug reactions in social media texts. Journal of Computer Science, 15(6), pp.873-879. DOI: https://doi.org/10.3844/jcssp.2019.873.879
Yousef, R.N.M., Tiun, S., Omar, N., and Alshari, E.M., 2020. Enhance medical sentiment vectors through document embedding using recurrent neural network. International Journal of Advanced Computer Science and Applications, 11(4). DOI: https://doi.org/10.14569/IJACSA.2020.0110452
Yousef, R.N.M., Tiun, S., Omar, N., and Alshari, E.M., 2020. Lexicon replacement method using word embedding technique for extracting adverse drug reaction. International Journal of Technology Management and Information System, 2(1), pp.113-122.
Yu, H., and Hatzivassiloglou, V., 2003. Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp.129-136. DOI: https://doi.org/10.3115/1119355.1119372
Zhang, M., and Geng, G., 2019. Adverse drug event detection using a weakly supervised convolutional neural network and recurrent neural network model. Information, 10(9), p.276. DOI: https://doi.org/10.3390/info10090276
Zhang, T., Lin, H., Ren, Y., Yang, Z., Wang, J., Duan, X., and Xu, B., 2021. Identifying adverse drug reaction entities from social media with adversarial transfer learning model. Neurocomputing, 453, pp.254-262. DOI: https://doi.org/10.1016/j.neucom.2021.05.007
Zhang, T., Lin, H., Xu, B., Ren, Y., Yang, Z., Wang, J., and Duan, X., 2020. Gated Iterative Capsule Network for Adverse Drug Reaction Detection from Social Media. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp.387-390. DOI: https://doi.org/10.1109/BIBM49941.2020.9313092
Zhang, W., Peissig, P., Kuang, Z., and Page, D., 2020. Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks. In: Proceedings of the ACM Conference on Health, Inference, and Learning, pp.30-39. DOI: https://doi.org/10.1145/3368555.3384459
Zhang, W., Yoshida, T., and Tang, X., 2008. Text classification based on multi-word with support vector machine. Knowledge-Based Systems, 21(8), pp.879-886. DOI: https://doi.org/10.1016/j.knosys.2008.03.044
Zhang, Y., Cui, S., and Gao, H., 2020. Adverse drug reaction detection on social media with deep linguistic features. Journal of Biomedical Informatics, 106, p.103437. DOI: https://doi.org/10.1016/j.jbi.2020.103437
Zhang, Z., Ye, Q., Zhang, Z., and Li, Y., 2011. Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), pp.7674-7682. DOI: https://doi.org/10.1016/j.eswa.2010.12.147
Zweigenbaum, P., Demner-Fushman, D., Yu, H., and Cohen, K.B., 2007. Frontiers of biomedical text mining: Current progress. Briefings in Bioinformatics, 8(5), pp.358-375. DOI: https://doi.org/10.1093/bib/bbm045
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