Artificial neural network: A data mining tool in pharmacovigilance

  • B. Mamatha St. Mary’s College of Pharmacy, Narakoduru, Guntur, India- 522212
  • P. Venkateswara Rao St. Mary’s College of Pharmacy, Narakoduru, Guntur, India- 522212
Keywords: Pharmacovigilance, adverse drug reactions, ADR assessment, neural networking


Introduction: Pharmacovigilance ensures patient safety as well as drug safety. In India, there is still a lot to be done and learned to ensure that the work and activities done in the area of pharmacovigilance are safely implemented. The key issue in India is that adverse drug reaction (ADR) has been underreported. The number of patients who are hospitalized is growing due to adverse drug effects and figuring out the exact cause of ADRs is a problem, if a patient is treated concurrently with several medicines.

Methods: In the analysis, we will analyze the various types of evaluation scale to conduct the ADR evaluation and identify the trigger agents. For situations where various approaches may not be adequate prognostic models, neural networks emerged as advanced data processing devices.

Results: However, it is essentially statistical modeling tools that are used in neural network models, as the term implies.

Conclusions: These models are thus a replacement solution, offering resources that learn by themselves, while not requiring experts or advanced computer programs, to solve problems and discern patterns.


Download data is not yet available.

Author Biographies

B. Mamatha, St. Mary’s College of Pharmacy, Narakoduru, Guntur, India- 522212

Department of Pharmaceutics

P. Venkateswara Rao, St. Mary’s College of Pharmacy, Narakoduru, Guntur, India- 522212

Department of Pharmaceutical Chemistry


Arnott, J., Hesselgreaves, H., Nunn, A. J., Peak, M., Pirmohamed, M., Smyth, R. L., Turner, M. A., & Young, B. (2013). What can we learn from parents about enhancing participation in pharmacovigilance? British journal of clinical pharmacology, 75(4), 1109–1117.

Coloma, P. M., Trifirò, G., Patadia, V., & Sturkenboom, M. (2013). Postmarketing safety surveillance: where does signal detection using electronic healthcare records fit into the big picture?. Drug safety, 36(3), 183–197.

Department of Health and Human Services. National Action Plan for Adverse Drug Event Prevention. (2014). Retrieved from: Accessed September 24, 2017.

Duggirala, H. J., Tonning, J. M., Smith, E., Bright, R. A., Baker, J. D., Ball, R., Bell, C., Bright-Ponte, S. J., Botsis, T., Bouri, K., Boyer, M., Burkhart, K., Condrey, G. S., Chen, J. J., Chirtel, S., Filice, R. W., Francis, H., Jiang, H., Levine, J., Martin, D., … Kass-Hout, T. (2016). Use of data mining at the Food and Drug Administration. Journal of the American Medical Informatics Association: JAMIA, 23(2), 428–434.

Gerritsen, R., Aronson, J. K. (2012). Adverse Drug Reactions: History, Terminology, Classification, Causality, Frequency, Preventability. In: John Talbot JKA, editors. Stephens' Detection and Evaluation of Adverse Drug Reactions: Principles and Practice. Chichester, UK: John Wiley & Sons, Ltd. pp. 1-119.

Gerritsen, R., Faddegon, H., Dijkers, F., van Grootheest, K., & van Puijenbroek, E. (2011). Effectiveness of Pharmacovigilance Training of General Practitioners A Retrospective Cohort Study in the Netherlands Comparing Two Methods. Drug Safety, 34(9), 755-762.

Harpaz, R., DuMochel, W., & Shah, N. H. (2016). Big Data and Adverse Drug Reaction Detection. Clinical pharmacology and therapeutics, 99(3), 268–270.

Kshirsagar, N., Ferner, R., Figueroa, B. A., Ghalib, H., & Lazdin, J. (2011). Pharmacovigilance methods in public health programmes: the example of miltefosine and visceral leishmaniasis. Transactions of the Royal Society of Tropical Medicine and Hygiene, 105(2), 61–67.

Leone, R., Magro, L., Moretti, U., Cutroneo, P., Moschini, M., Motola, D., Tuccori, M., & Conforti, A. (2010). Identifying adverse drug reactions associated with drug-drug interactions: data mining of a spontaneous reporting database in Italy. Drug safety, 33(8), 667–675.

Lindquist, M., Edwards, I. R., Bate, A., Fucik, H., Nunes, A. M., & Ståhl, M. (1999). From association to alert--a revised approach to international signal analysis. Pharmacoepidemiology and drug safety, 8 Suppl 1, S15–S25.

Lindquist, M., Ståhl, M., Bate, A., Edwards, I. R., & Meyboom, R. H. (2000). A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug safety, 23(6), 533–542.

Maclure, M., Fireman, B., Nelson, J. C., Hua, W., Shoaibi, A., Paredes, A., & Madigan, D. (2012). When should case-only designs be used for safety monitoring of medical products? Pharmacoepidemiology and drug safety, 21 Suppl 1, 50–61.

Manson, J. M., Freyssinges, C., Ducrocq, M. B., & Stephenson, W. P. (1996). Postmarketing surveillance of lovastatin and simvastatin exposure during pregnancy. Reproductive toxicology (Elmsford, N.Y.), 10(6), 439–446.

Pipasha, B., Biswas, A. K. (2007). Setting standards for proactive pharmacovigilance in India: The way forward. Indian journal of pharmacology, 39(3), 124-128.

Skalli, S., & Soulaymani Bencheikh, R. (2012). Safety monitoring of herb-drug interactions: a component of pharmacovigilance. Drug safety, 35(10), 785–791.

Stahl, M., Lindquist, M., Edwards, I. R., & Brown, E. G. (2004). Introducing triage logic as a new strategy for the detection of signals in the WHO Drug Monitoring Database. Pharmacoepidemiology and drug safety, 13(6), 355–363.

Warrer, P., Hansen, E. H., Juhl-Jensen, L., & Aagaard, L. (2012). Using text-mining techniques in electronic patient records to identify ADRs from medicine use. British journal of clinical pharmacology, 73(5), 674–684.

WHO. (2004). Pharmacovigilance: Ensuring the Safe Use of Medicines. Geneva: WHO.

Witten, I. H., Frank, E. (1999). Data mining: practical machine learning tools and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann Publishers.

How to Cite
B. Mamatha, P. Venkateswara Rao. Artificial neural network: A data mining tool in pharmacovigilance . jpadr [Internet]. 2020Sep.1 [cited 2023Jun.9];1(1):1-. Available from: