Adverse Drug Reaction Detection with Drug Prediction Using BERT?LSTM?SVM
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2026.20.3.7Keywords:
adverse drug reactions, BERT, long short-term memory, natural language processing, support vector machine, treatment recommendationAbstract
Social media can be a useful resource for assessing patient-reported drug experiences. Adverse drug reactions (ADRs) are an important public health issue, but finding social media posts in which adverse reactions are described is a difficult task, since not all posts are related to ADR. The proposed work in this research is a hybrid model of BERT-LSTM-SVM for detection of ADRs and drug classification. The input is preprocessed as follows: the text is cleaned, tokenized, normalized, and noise is removed. Bidirectional Encoder Representations from Transformers (BERT) is used to produce contextual representations of the text, and a Long Short-Term Memory network (LSTM) is used to model sequential dependencies to aid binary classification of ADR and non-ADR text. Then, a Support Vector Machine (SVM) classifier is used to classify the associated medicine based on the extracted textual features. In our validation of the method, the accuracy, sensitivity, specificity and precision were 0.99 with a recall. The proposed framework combines contextual language representations, sequential modeling, and supervised classification approaches to facilitate the automatic detection of ADRs and medicine identification.
Downloads
References
Fan, B., Fan, W., Smith, C. & Garner, H., Adverse Drug Event Detection and Extraction from Open Data: A Deep Learning Approach, Information Processing & Management, 57(1), 102131, 2020.
Rezaei, Z., Ebrahimpour-Komleh, H., Eslami, B., Chavoshinejad, R. & Totonchi, M., Adverse Drug Reaction Detection in social media by Deep Learning Methods, Cell Journal (Yakhteh), 22(3), 319, 2020.
Florez, E., Precioso, F., Pighetti, R. & Riveill, M., Deep Learning for Identification of Adverse Drug Reaction Relations, Proceedings of the 2019 International Symposium on Signal Processing Systems, pp. 149-153, 2019.
Alimova, I. & Tutubalina, E., Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pp. 415-421, 2019.
Zhang, T., Lin, H., Xu, B., Ren, Y., Yang, Z., Wang, J. & Duan, X., Gated Iterative Capsule Network for Adverse Drug Reaction Detection from social media, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 387?390, 2020.
McNeil, B.D., MRGPRX2 and Adverse Drug Reactions, Frontiers in Immunology, 12, 676354, 2021.
Zazzara, M.B., Palmer, K., Vetrano, D.L., Carf A. & Graziano, O., Adverse Drug Reactions in Older Adults: A Narrative Review of the Literature, European Geriatric Medicine, 12, pp. 463-473, 2021.
Zucker, I. & Prendergast, B.J., Sex Differences in Pharmacokinetics Predict Adverse Drug Reactions in Women, Biology of Sex Differences, 11, pp. 1-14, 2020.
Magro, L., Arzenton, E., Leone, R., Stano, M.G., Vezzaro, M., Rudolph, A., Castagna, I. & Moretti, U., Identifying and Characterizing Serious Adverse Drug Reactions Associated with Drug-drug Interactions in a Spontaneous Reporting Database, Frontiers in Pharmacology, 11, 622862, 2021.
Ghasemiyeh, P., Borhani-Haghighi, A., Karimzadeh, I., Mohammadi-Samani, S., Vazin, A., Safari, A. & Qureshi, A.I., Major Neurologic Adverse Drug Reactions, Potential Drug?drug Interactions and Pharmacokinetic Aspects of Drugs used in COVID-19 Patients with Stroke: A Narrative Review, Therapeutics and Clinical Risk Management, pp. 595-605, 2021.
Wang, C.-S., Lin, P.-J., Cheng, C.-L., Tai, S.-H., Yang, Y.-H.K. & Jung-Hsien Chiang., Detecting Potential Adverse Drug Reactions using a Deep Neural Network Model, Journal of Medical Internet Research, 21(2), -e11016, 2019.
Zhang, M. & Geng, G., Adverse Drug Event Detection using a Weakly Supervised Convolutional Neural Network and Recurrent Neural Network Model, Information, 10(9), 276, 2019.
Yousef, R.N.M., Tiun, S., Omar, N. & Alshari, E.M., 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, 2019.
Zhang, T., Lin, H., Ren, Y., Yang, L., Xu, B., Yang, Z., Wang, J. & Zhang, Y., Adverse Drug Reaction Detection via a Multihop Self-attention Mechanism, BMC Bioinformatics, 20(1), pp. 1-11, 2019.
Joshi, P., Vedhanayagam, M. & Ramesh, R., An Ensembled SVM based Approach for Predicting Adverse Drug Reactions, Current Bioinformatics, 16(3), pp. 422-432, 2021.
Xiang, W.J., Lu, Z., Qiu, K., Li, P. & Sun, H., Predicting Drug Risk Level from Adverse Drug Reactions using SMOTE and Machine Learning Approaches, IEEE Access, 8, pp. 185761-185775, 2020.
Mantripragada, A.S., Teja, S.P., Katasani, R.R., Joshi, P., V, M. & Ramesh, R., Prediction of Adverse Drug Reactions using Drug Convolutional Neural Networks, Journal of Bioinformatics and Computational Biology, 19(1), 2050046, 2021.
Portelli, B., Passab D., Lenzi, E., Serra, G., Santus, E. & Chersoni, E., Improving Adverse Drug Event Extraction with Spanbert on Different Text Typologies, International Workshop on Health Intelligence, pp. 87-99, 2021.
Zhang, F., Sun, B., Diao, X., Zhao, W. & Shu, T., Prediction of Adverse Drug Reactions based on Knowledge Graph Embedding, BMC Medical Informatics and Decision Making, 21, pp. 1-11, 2021.
Lee, C.Y., & Chen, Y.-P.P., Prediction of Drug Adverse Events using Deep Learning in Pharmaceutical Discovery, Briefings in Bioinformatics, 22(2), pp. 1884-1901, 2021.
Mahendran, Darshini, & Bridget T. McInnes., Extracting Adverse Drug Events from Clinical Notes, AMIA Summits on Translational Science Proceedings, 2021, pp. 420, 2021.
Christopoulou, F., Tran, T.T., Sahu, S.K., Miwa, M. & Ananiadou, S., Adverse Drug Events and Medication Relation Extraction in Electronic Health Records with Ensemble Deep Learning Methods, Journal of the American Medical Informatics Association, 27(1), pp. 39-46, 2020.
Magge, A., Tutubalina, E., Miftahutdinov, Z., Alimova, I., Dirkson, A., Verberne, S., Weissenbacher, D. & Gonzalez-Hernandez, G., Deepademiner: A Deep Learning Pharmacovigilance Pipeline for Extraction and Normalization of Adverse Drug Event Mentions on Twitter, Journal of the American Medical Informatics Association, 28(10), pp. 2184-2192, 2021.
Haq, H.U., Kocaman, V. & Talby, D., Mining Adverse Drug Reactions from Unstructured Mediums at Scale, In Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence, pp. 361-375, 2022.
Lee, S., Woo, H., Lee, C.C., Kim, G., Kim, J.-Y. & Lee, S., Drug_SNSMiner: Standard Pharmacovigilance Pipeline for Detection of Adverse Drug Reaction Using SNS Data, Scientific Reports, 13(1), 3779, 2023.
Linier, L., Jouganous, J., Benkebil, M., Bel?Loile, A., Goehrs, C., Singier, A., Rouby, F., Lacroix, C., Miremont, G., Micallef, J., Salvo, F. & Pariente, A., Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions, Clinical Pharmacology & Therapeutics, 110(2), pp. 392-400, 2021.
Nyamabo, A.K., Yu, H. & Shi, J.-Y., SSI?DDI: Substructure?Substructure Interactions for Drug?Drug Interaction Prediction, Briefings in Bioinformatics, 22(6), bbab133, 2021.
Chen, Y., Ma, T., Yang, X., Wang, J., Song, B. & Zeng, X., MUFFIN: Multi-scale Feature Fusion for Drug?drug Interaction Prediction, Bioinformatics, 37(17), pp. 2651?2658, 2021.
Hussain, S., Afzal, H., Saeed, R., Iltaf, N. & Umair, M.Y., Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions using BERT Fine-tuned with FARM, Computational and Mathematical Methods in Medicine, 2021.
Breden, A. & Moore, L., Detecting Adverse Drug Reactions from Twitter Through Domain-specific Preprocessing and BERT Ensembling, arXiv Preprint arXiv:2005.06634, 2020.
Nishioka, S., Watanabe, T., Asano, M., Yamamoto, T., Kawakami, K., Yada, S., Aramaki, E., Yajima, H., Kizaki, H. & Hori, S., Identification of Hand-foot Syndrome from Cancer Patients? Blog Posts: BERT-based Deep-learning Approach to Detect Potential Adverse Drug Reaction Symptoms, PLOS ONE, 17(5), pp. e0267901, 2022.
Portelli, B., Lenzi, E., Chersoni, E., Serra, G. & Santus, E., BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 1740-1747, 2021.
Heo, E.Y., Jeong, H-j. & Kim, H.H., Detection of Adverse Drug Reactions using Drug Reviews with BERT+ Algorithm, KIPS Transactions on Software & Data Engineering, 10(11), pp. 465-472, 2021.
Qian, J., Qiu, X., Tan, X., Li, Q., Chen, J. & Jiang, X., An Attentive LSTM based Approach for Adverse Drug Reactions Prediction, Applied Intelligence, 53(5), pp. 4875-4889, 2023.
Alshaikhdeeb, A. J., & Yu-N. Cheah., Utilizing Word Index Approach with LSTM Architecture for Extracting Adverse Drug Reaction from Medical Reviews, Journal of Advances in Information Technology, 14(3), 2023.
Wang, X., Wang, X. & Zhang, S., Adverse Drug Reaction Detection from Social Media Based on Quantum Bi-LSTM with Attention, IEEE Access, 11, pp. 16194-16202, 2022.
El-allaly, E.D., Sarrouti, M., En-Nahnahi, N. & El Alaoui, S.O., A LSTM-based Method with Attention Mechanism for Adverse Drug Reaction Sentences Detection, In Advanced Intelligent Systems for Sustainable Development (AI2SD?2019), pp. 17-26, 2020.
Grissette, H. & Nfaoui, E.H. Adversarial LSTM-based Sequence-to-sequence Model for Drug-related Reactions Understanding, Proceedings of Sixth International Congress on Information & Communication Technology (ICICT 2021), pp. 49-59, 2022.
Wang, Xuqi, Wenzhun Huang, & Shanwen Zhang., Social Media Adverse Drug Reaction Detection Based on Bi-LSTM with Multi-head Attention Mechanism, Intelligent Computing Theories & Application: 17th International Conference (ICIC 2021), pp. 57-65, 2021.
Islam, T., Hussain, N., Islam, S. & Chakrabarty, A., Detecting Adverse Drug Reaction with Data Mining & Predicting its Severity with Machine Learning, 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 1-5, 2018.
Li, Z., Lin, H. & Zheng, W., An Effective Emotional Expression & Knowledge-enhanced Method for Detecting Adverse Drug Reactions. IEEE Access, 8, pp. 87083-87093, 2020.
Chaichulee, S., Promchai, C., Kaewkomon, T., Kongkamol, C., Ingviya, T. & Sangsupawanich, P., Multi-label Classification of Symptom Terms from Free-text Bilingual Adverse Drug Reaction Reports using Natural Language Processing, PLOS ONE, 17(8), pp. e0270595, 2022.
Sakhovskiy, A., Miftahutdinov, Z. & Tutubalina, E., KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects, Proceedings of the Sixth Social Media Mining for Health (SMM4H) Workshop & Shared Task, pp. 39-43, 2021.
Tutubalina, E., Alimova, I., Miftahutdinov, Z., Sakhovskiy, A., Malykh, V. & Nikolenko, S., The Russian Drug Reaction Corpus & Neural Models for Drug Reactions and Effectiveness Detection in user Reviews, Bioinformatics, 37(2), pp. 243-249, 2021.
