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Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection


  • Ahmed Adil Nafea Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
  • Nazlia Omar Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
  • Zohaa Mubarak Al-qfail Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia



Adverse Drug Reaction, Artificial Neural Network, Classification, Deep Learning, Latent Semantic Analysis


Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting ADR.


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Zhang T, Lin H, Xu B, Yang L, Wang J, Duan X. Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions. J Biomed Inform. 2021; 123: 103896.

Zhang T, Lin H, Ren Y, Yang Z, Wang J, Duan X, et al. Identifying adverse drug reaction entities from social media with adversarial transfer learning model. Neurocomputing. 2021; 453: 254–62.

Ray SD, Luzum JA, Gray JP, Stohs SJ. Focus on pharmacogenomics, phytonutrient-drug interactions and COVID-19 vaccines: Perspectives on ADRs, ADEs, and SEDs. Side Eff Drugs Annu. 2021; 43:xxv.

Li R, Curtis K, Zaidi ST, Van C, Castelino R. A new paradigm in adverse drug reaction reporting: consolidating the evidence for an intervention to improve reporting. Expert Opin Drug Saf. 2022; 21(9): 1193–204.

Ebrahimi M, Yazdavar AH, Salim N, Eltyeb S. Recognition of side effects as implicit-opinion words in drug reviews. Online Inf Rev. 2016; 40(7): 1018–32.

Kumar NH, Narendra B, Upendra K, Rajesh K. A review on Adverse drug reactions monitoring and reporting. Int J Pharm Res Technol. 2019; 9(2): 12–5.

Yousef RNM, Tiun S, Omar N, Alshari EM. Enhance medical sentiment vectors through document embedding using recurrent neural network. Int J Adv Comput Sci Appl. 2020; 11(4): 372-378.

Siuly S, Lee I, Huang Z, Zhou R, Wang H, Xiang W. Health Information Science. 1st ed. Springer International Publishing; 2018.

Kiritchenko S, Mohammad SM, Morin J, de Bruijn B. NRC-Canada at SMM4H shared task: classifying Tweets mentioning adverse drug reactions and medication intake. arXiv Prepr arXiv180504558. 2018.

Yousef RN, Tiun S, Omar N. Extended Trigger Terms for Extracting Adverse Drug Reactions in Social Media Texts. J Comput Sci. 2019; 15(6): 873–9.

Pain J, Levacher J, Quinquenel A, Belz A. Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilanc. In: Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT). COLING 2016 Organ Commit. 2016 : 94–101.

Plachouras V, Leidner JL, Garrow AG. Quantifying self-reported adverse drug events on Twitter: signal and topic analysis. Proc 7th Int Conf Soc Media Soc. ACM Press. 2016: 1–10.

Nafea AA, Omar N, AL-Ani MM. Adverse Drug Reaction Detection Using Latent Semantic Analysis. J Comput Sci. 2021; 17(10): 960–70.

Li Z, Yang Z, Luo L, Xiang Y, Lin H. Exploiting adversarial transfer learning for adverse drug reaction detection from texts. J Biomed Inform. 2020; 106: 103431.

Al-Shareeda MA, Manickam S, Laghari SA, Jaisan A. Replay-Attack Detection and Prevention Mechanism in Industry 4.0 Landscape for Secure SECS/GEM Communications. Sustainability. 2022; 14(23): 15900.

Al-Shareeda MA, Manickam S. COVID-19 Vehicle Based on an Efficient Mutual Authentication Scheme for 5G-Enabled Vehicular Fog Computing. Int J Environ Res Public Health. 2022; 19(23): 15618.

Al-Sabahi K, Zhang Z, Long J, Alwesabi K. An enhanced latent semantic analysis approach for arabic document summarization. Arab J Sci Eng. 2018; 43(12): 8079–94.

Emadzadeh E, Sarker A, Nikfarjam A, Gonzalez G. Hybrid semantic analysis for mapping adverse drug reaction mentions in tweets to medical terminology. AMIA Annu Symp Proc. PubMed Central. 2018: 679–688. PMCID: PMC5977584

Yates A, Goharian N. ADRTrace: detecting expected and unexpected adverse drug reactions from user reviews on social media sites. IAdvances in Information Retrieval: 35th European Conference on IR Research. 2013, Moscow, Russia, March 24-27, 2013 Proceedings 35. Springer Berlin Heidelberg; 2013: 816–9.

Abdulmajeed AA, Tawfeeq TM, Al-jawaherry MA. Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning. Baghdad Sci J. 2022; 19(3): 642.

Kaur J, Buttar PK. A systematic review on stopword removal algorithms. Int J Futur Revolut Comput Sci Commun Eng. 2018; 4(4): 207–10.

Oliinyk VA, Vysotska V, Burov Y, Mykich K, Fernandes V. Propaganda detection in text data based on NLP and machine learning. In: CEUR Workshop Proceedings. MoMLeT+DS. 2020: 132–44.

Chary M, Parikh S, Manini AF, Boyer EW, Radeos M. A review of natural language processing in medical education. West J Emerg Med. 2019; 20(1): 78.

Patel R, Passi K. Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning. IoT. 2020; 1(2): 218–39.

Mohammed M, Omar N. Question classification based on Bloom’s taxonomy cognitive domain using modified TF-IDF and word2vec. PLoS One. 2020; 15(3): e0230442.

Kayesh H, Islam MS, Wang J, Ohira R, Wang Z. SCAN: A shared causal attention network for adverse drug reactions detection in tweets. Neurocomputing. 2022; 479: 60–74.

Rabani ST, Khan QR, Khanday A. Detection of suicidal ideation on Twitter using machine learning & ensemble approaches. Baghdad Sci J. 2020; 17(4): 1328.

Filieri R, Galati F, Raguseo E. The impact of service attributes and category on eWOM helpfulness: An investigation of extremely negative and positive ratings using latent semantic analytics and regression analysis. Comput Human Behav. 2021; 114: 106527.

Hassan FH, Omar MA. Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation. Baghdad Sci J. 2021; 18(4 Supplement).

AL-Ani MM, Omar N, Nafea AA. A Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm Detection. J Comput Sci. 2021; 17(11): 1093–8.

Lee K, Qadir A, Hasan SA, Datla V, Prakash A, Liu J, et al. Adverse drug event detection in tweets with semi-supervised convolutional neural networks. Proc 26th int conf world wide web. ACM Press. 2017: 705–14. .

Cocos A, Fiks AG, Masino AJ. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J Am Med Informatics Assoc. 2017; 24(4): 813–21.

Wang CS, Lin PJ, Cheng CL, Tai SH, Yang YHK, Chiang JH. Detecting potential adverse drug reactions using a deep neural network model. J Med Internet Res. 2019; 21(2): e11016.