APPLICATION OF MACHINE LEARNING APPROACHES FOR PREDICTION AND CLASSIFICATION OF ADVERSE DRUG REACTIONS IN PHARMACOVIGILANCE
Abstract
Adverse drug reactions remain a major concern in pharmacovigilance because they contribute to patient morbidity, treatment interruption, hospitalization, and increased healthcare burden. This study aimed to apply machine learning approaches for the prediction and classification of adverse drug reactions as serious or non-serious using primary quantitative data. A cross-sectional study design was adopted, and data were collected from 300 participants using a structured adverse drug reaction assessment form. Information on demographic characteristics, clinical profile, medication history, drug class, route of administration, number of concomitant medicines, reaction type, causality, severity, seriousness, and outcome was obtained. The collected data were cleaned, coded, and analyzed using descriptive statistics and supervised machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting. Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and ROC-AUC. Gastrointestinal reactions were the most common adverse drug reactions, and 27.3% of cases were classified as serious. Serious reactions were significantly associated with age, comorbidity, polypharmacy, parenteral drug administration, and ADR severity. Random Forest showed the best performance, with an accuracy of 0.89 and an ROC-AUC of 0.92. The findings suggest that machine learning, particularly ensemble-based models, may support early ADR seriousness classification and strengthen pharmacovigilance decisionmaking.
Downloads
References
Bae, J. H., Baek, Y. H., Lee, J. E., Song, I., Lee, J. H., & Shin, J. Y. (2021). Machine learning for detection of safety
signals from spontaneous reporting system data: example of nivolumab and docetaxel. Frontiers in
Pharmacology, 11, 602365.
Ball, R., & Dal Pan, G. (2022). “Artificial intelligence” for pharmacovigilance: ready for prime time?. Drug
safety, 45(5), 429-438.
Cherkas, Y., Ide, J., & van Stekelenborg, J. (2022). Leveraging machine learning to facilitate individual case causality
assessment of adverse drug reactions. Drug Safety, 45(5), 571-582.
Correia Pinheiro, L., Durand, J., & Dogné, J. M. (2020). An application of machine learning in pharmacovigilance:
estimating likely patient genotype from phenotypical manifestations of fluoropyrimidine toxicity. Clinical
Pharmacology & Therapeutics, 107(4), 944-947.
Edrees, H., Song, W., Syrowatka, A., Simona, A., Amato, M. G., & Bates, D. W. (2022). Intelligent telehealth in
pharmacovigilance: a future perspective. Drug safety, 45(5), 449-458.
Feng, Z. Y., Wu, X. H., Ma, J. L., Li, M., He, G. F., Cao, D. S., & Yang, G. P. (2023). DKADE: a novel framework
based on deep learning and knowledge graph for identifying adverse drug events and related medications. Briefings
in bioinformatics, 24(4), bbad228.
Galeano, D., & Paccanaro, A. (2022). Machine learning prediction of side effects for drugs in clinical trials. Cell
reports methods, 2(12).
Galletti, C., Aguirre-Plans, J., Oliva, B., & Fernandez-Fuentes, N. (2022). Prediction of adverse drug reaction linked
to protein targets using network-based information and machine learning. Frontiers in Bioinformatics, 2, 906644.
Guellil, I., Wu, J., Gema, A. P., Francis, F., Berrachedi, Y., Chenni, N., ... & Alex, B. (2024). Natural language
processing for detecting adverse drug events: A systematic review protocol. NIHR Open Research, 3, 67.
Haerdtlein, A., Debold, E., Rottenkolber, M., Boehmer, A. M., Pudritz, Y. M., Shahid, F., ... & Dreischulte, T. (2023).
Which adverse events and which drugs are implicated in drug-related hospital admissions? A systematic review and
meta-analysis. Journal of Clinical Medicine, 12(4), 1320.
Hamid, A. A. T. A., Rahim, R., & Teo, S. P. (2022). Pharmacovigilance and its importance for primary health care
professionals. Korean journal of family medicine, 43(5), 290.
Hussain, R. (2021). Big data, medicines safety and pharmacovigilance. Journal of pharmaceutical policy and
practice, 14(1), 48.
Khouri, C., Petit, C., Tod, M., Lepelley, M., Revol, B., Roustit, M., & Cracowski, J. L. (2021). Adverse drug reaction
risks obtained from meta-analyses and pharmacovigilance disproportionality analyses are correlated in most
cases. Journal of clinical epidemiology, 134, 14-21.
Knisely, B. M., Hatim, Q., & Vaughn-Cooke, M. (2022). Utilizing deep learning for detecting adverse drug events
in structured and unstructured regulatory drug data sets. Pharmaceutical Medicine, 36(5), 307-317.
Lee, J. E., Kim, J. H., Bae, J. H., Song, I., & Shin, J. Y. (2022). Detecting early safety signals of infliximab using
machine learning algorithms in the Korea adverse event reporting system. Scientific Reports, 12(1), 14869.
Martin, G. L., Jouganous, J., Savidan, R., Bellec, A., Goehrs, C., Benkebil, M., ... & French Network of
Pharmacovigilance Centres. (2022). Validation of artificial intelligence to support the automatic coding of patient
adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Safety, 45(5), 535-548.
Pilipiec, P., Liwicki, M., & Bota, A. (2022). Using machine learning for pharmacovigilance: a systematic
review. Pharmaceutics, 14(2), 266.
Salas, M., Petracek, J., Yalamanchili, P., Aimer, O., Kasthuril, D., Dhingra, S., ... & Bostic, T. (2022). The use of
artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceutical medicine, 36(5),
-306.
Ye, X., Li, Y., & Yao, L. (2023, September). Dream: Decoupled representation via extraction attention module and
supervised contrastive learning for cross-domain sequential recommender. In Proceedings of the 17th ACM
conference on recommender systems (pp. 479-490).
Zhao, H., Ni, P., Zhao, Q., Liang, X., Ai, D., Erhardt, S., ... & Wang, J. (2023). Identifying the serious clinical
outcomes of adverse reactions to drugs by a multi-task deep learning framework. Communications Biology, 6(1),
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal For Research In Biology & Pharmacy
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
In consideration of the journal, Green Publication taking action in reviewing and editing our manuscript, the authors undersigned hereby transfer, assign, or otherwise convey all copyright ownership to the Editorial Office of the Green Publication in the event that such work is published in the journal. Such conveyance covers any product that may derive from the published journal, whether print or electronic. Green Publication shall have the right to register copyright to the Article in its name as claimant, whether separately
or as part of the journal issue or other medium in which the Article is included.
By signing this Agreement, the author(s), and in the case of a Work Made For Hire, the employer, jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere, and is not being considered for publication elsewhere in any form, except as provided herein. Each author’s signature should appear below. The signing author(s) (and, in
