The Pharmacovigilance using AI

Main Article Content

Rakibul Asheeque
Srivathshan KS
Parvej Saleh


The Adverse Drug Effects (ADEs) are the harmful reaction towards a prescribed medicine. Pharmacovigilance is the effort to detect, assess and prevent ADEs. ADE detection in one of the most essential objective of post-marketing stage. Food and Drug Administration (FDA) uses the Adverse Event Reporting System (AERS) to monitor the reports of ADEs from pharmaceutical companies, doctors, hospital, patients and pharmacies. The major drawback is incomplete information, over-reporting on already documented ADEs and under-reporting of new ADEs. This paper concentrates on collecting tweets from Twitter which has the mentioned drug and identify the Adverse Drug Effects associated using NLP and classify them. The newly detected ADEs are reported by checking the detected ADE in a database where already reported ADEs are mentioned.

Article Details

How to Cite
AsheequeR., KSS., & SalehP. (2020). The Pharmacovigilance using AI. Probyto Journal of AI Research, 1(01). Retrieved from


[1] R Sloane et al, “Social media and pharmacovigilance: A review of the opportunities and challenges” in British Journal of Clinical Pharmacology.
[2] Samuel Stallin Kapembe and José Quenum, “Lihonga — a Microservice-based Virtual Learning Environment” in 18th International Conference on Advanced Learning Technologies, 2018, IEEE.
[3] Deeraj Nagothu et al., “A Microservice-enabled Architecture for Smart Surveillance using Blockchain Technology”, IEEE.
[4] Nikfarjam et al, “Pharmacovigilance from social media” in Journal of the American Medical Informatics Association.

Most read articles by the same author(s)

1 2 > >>