Machine learning in drug safety: exploring applications and addressing challenges for improved patient safety

  • Riya Jose Rajiv Gandhi University of health sciences
Keywords: Artificial intelligence, Machine Learning, Pharmacovigilance, Drug safety, Applications


Artificial intelligence (AI) has emerged as a powerful tool in the pharmaceutical industry, utilizing machine learning and natural language processing to access and analyze vast amounts of research and information. AI has a wide range of applications in the field of pharmacovigilance (PV) and drug safety, including the improvement of information retrieval, the identification of medical concepts, text translation, automated illness image classification, and the gathering of adverse drug response (ADR) data. This review article explores the potential of AI in drug safety and its challenges, highlighting its applications along with examples. By embracing AI advancements, the industry can enhance drug safety monitoring, improve data analysis, and contribute to better healthcare outcomes. 


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Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., & Naidich, D. P. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954-961.

Barry, A., Olsson, S., Minzi, O., Bienvenu, E., Makonnen, E., Kamuhabwa, A., Oluka, M., Guantai, A., Bergman, U., van Puijenbroek, E., & Gurumurthy, P. (2020). Comparative assessment of the national pharmacovigilance systems in East Africa: Ethiopia, Kenya, Rwanda and Tanzania. Drug Safety, 43, 339-350.

Bate, A., & Hobbiger, S. F. (2021). Artificial Intelligence, Real-World Automation and the Safety of Medicines. Drug Safety, 44(2), 125-132.

Chen, Y., Wang, Y., Wang, N., Xiang, Y., Zhang, R., Xiao, J., Liu, H., & Feng, B. (2021). Knowledge, attitude, and practice regarding pharmacovigilance among the general public in Western China: A cross-sectional study. Current Medical Research and Opinion, 37(1), 101-108.

Comfort, S., Perera, S., Hudson, Z., Dorrell, D., Meireis, S., Nagarajan, M., Ramakrishnan, C., & Fine, J. (2018). Sorting through the safety data haystack: Using machine learning to identify individual case safety reports in social-digital media. Drug Safety, 41(5), 579-590.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Gartland, A., Bate, A., Painter, J. L., Casperson, T. A., & Powell, G. E. (2021). Developing crowdsourced training data sets for pharmacovigilance intelligent automation. Drug Safety, 44, 373-382.

Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023). Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discovery Artificial Intelligence, 3(1), 5.

Houssein, E. H., Mohamed, R. E., & Ali, A. A. (2021). Machine learning techniques for biomedical natural language processing: A comprehensive review. IEEE Access, 9, 140628-140653.

Hunter, J. (2016). Adopting AI is essential for a sustainable pharma industry. Drug Discovery World, 69-71.

Kimia, A. A., Savova, G., Landschaft, A., & Harper, M. B. (2015). An introduction to natural language processing: How you can get more from those electronic notes you are generating. Pediatric Emergency Care, 31(7), 536-541.

Lamprecht, I. V. (2011). An investigation into the prospects of existing technologies to address the challenges faced in pharmacovigilance systems. (Doctoral dissertation). Stellenbosch University.

Lardon, J., Abdellaoui, R., Bellet, F., Asfari, H., Souvignet, J., Texier, N., Jaulent, M. C., Beyens, M. N., Burgun, A., & Bousquet, C. (2015). Adverse drug reaction identification and extraction in social media: A scoping review. Journal of Medical Internet Research, 17(7), e171.

Luo, Y., Thompson, W. K., Herr, T. M., Zeng, Z., Berendsen, M. A., Jonnalagadda, S. R., Carson, M. B., & Starren, J. (2017). Natural language processing for EHR-based pharmacovigilance: A structured review. Drug Safety, 40, 1075-1089.

Ly, T., Pamer, C., Dang, O., Brajovic, S., Haider, S., Botsis, T., Milward, D., Winter, A., Lu, S., & Ball, R. (2018). Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology. Journal of Biomedical Informatics, 83, 73-86.

Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773-780.

Mohammadi, Y., Ghasemian, F., Varshosaz, J., & Sattari, M. (2023). Classifying referring/non-referring ADR in biomedical text using deep learning. Informatics in Medicine Unlocked, 39, 101246.

Nikfarjam, A., Sarker, A., O'Connor, K., Ginn, R., & 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), 671-681.

Park, M. Y., Yoon, D., Choi, N. K., Lee, J., Lee, K., Lim, H. S., Park, B. J., Kim, J. H., & Park, R. W. (2012). Construction of an open-access QT database for detecting the proarrhythmia potential of marketed drugs: ECG-ViEW. Clinical Pharmacology & Therapeutics, 92(3), 393-396.

Pharmafocus America. (n.d.). Automation in Pharmacovigilance [Internet]. Retrieved from

Prabhakar, S. K., & Won, D. O. (2021). Medical text classification using hybrid deep learning models with multihead attention. Computational Intelligence and Neuroscience, 2021, 2021.

Puri, M. (2020). Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images. Assay and Drug Development Technologies, 18(1), 1-10.

Raja, K., & Jonnalagadda, S. (2015). Natural Language Processing and Data Mining for Clinical Text. Healthcare Data Analytics, 36, 219.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160.

Shahbazi, Z., & Byun, Y. C. (2021). Blockchain-based event detection and trust verification using natural language processing and machine learning. IEEE Access, 10, 5790-5800.

Sheikhalishahi, S., Miotto, R., Dudley, J. T., Lavelli, A., Rinaldi, F., & Osmani, V. (2019). Natural language processing of clinical notes on chronic diseases: A systematic review. JMIR Medical Informatics, 7(2), e12239.

Sloane, R., Osanlou, O., Lewis, D., Bollegala, D., Maskell, S., & Pirmohamed, M. (2015). Social media and pharmacovigilance: A review of the opportunities and challenges. British Journal of Clinical Pharmacology, 80(4), 910-920.

Souvignet, J., Declerck, G., Asfari, H., Jaulent, M. C., & Bousquet, C. (2016). OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval. Journal of Biomedical Informatics, 63, 100-107.

Suke, S. G., Kosta, P., & Negi, H. (2015). Role of Pharmacovigilance in India: An overview. Online Journal of Public Health Informatics, 7(2), e223.

Tao, C., Li, Y., Zhu, C., Liu, L., Li, X., & Chute, C. G. (2017). Crowdsourcing annotations of adverse drug reactions in Twitter. AMIA Annual Symposium Proceedings, 1582-1591.

Wang, W., Haerian, K., Salmasian, H., Harpaz, R., Chase, H., & Friedman, C. (2011). A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations. In AMIA annual symposium proceedings (Vol. 2011, p. 1464). American Medical Informatics Association.

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, L., Zhao, T., Dai, J., Yang, J., Lan, L., Luo, M., Liu, Z., An, T., Zhang, B., He, X., Cong, S., Liu, X., Zhang, W., Lewis, J. P., Tiedje, J. M., Wang, Q., An, Z., Wang, F., Zhang, L., Huang, T., Lu, C., Cai, Z., Wang, F., Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179.

Zhao, Y., Wang, T., Li, G., Sun, S. (2018). Pharmacovigilance in China: Development and challenges. International Journal of Clinical Pharmacy, 40(4), 823-831.

Zhao, Y., Yu, Y., Wang, H., Li, Y., Deng, Y., Jiang, G., & Luo, Y. (2022). Machine Learning in Causal Inference: Application in Pharmacovigilance. Drug Safety, 45(5), 459-476.

How to Cite
Jose R. Machine learning in drug safety: exploring applications and addressing challenges for improved patient safety. jpadr [Internet]. 2023Sep.1 [cited 2023Dec.9];4(3):6-12. Available from: