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Adverse Drug Reaction Monitoring: Pharmacovigilance and Artificial Intelligence
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| Author:
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NEELAM INJETI, DR.ABHISEK PAL
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| Abstract:
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Adverse Drug reactions (ADRs) stay as a test in current medical care, especially given the expanding intricacy
of therapeutics, an ageing population and rising multimorbidity. Pharmacovigilance aims at collection, evaluation
and reporting of unfavourable functions known as individual Case safety report and consistent observation,
understanding and correspondence to product benefit -risk profile and validate Signal Detection and BenefitRisk Management. A cautious prescription history can help a prescriber in understanding the patient’s past
experiences with drug treatment, especially in distinguishing past ADRs that may block re-exposure to the
medication. The most common technique to identify ADRs is depending on spontaneous reports.
Unfortunately, the low revealing pace of spontaneous reports is a genuine limitation of Pharmacovigilance. This
article sums up a portion of the vital realities about ADRs reporting and Artificial Intelligence. In light of
expanding number of ICSR reporting, the dealing with and preparing of these reports is getting monotonous
and tedious occupation including the significant expenses. Consequently, PvPI may likewise embrace the
Machine learning (ML) strategies for diminishing labour force, cost and time for ICSR processing. By utilizing
information mining and taking electronic Health records (EHRs) into thought, AI encourages PV to give better
drug and improved health assistance. This review concludes that AI-empowered supportive networks, when
executed effectively, can help in upgrading patient safety.
Method: A systematic search to identify and retrieve relevant articles/studies in the PubMed, Medline, Scopus
by Google search engine.
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| Keyword:
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Pharmacovigilance programme India (PvPI) Adverse drug reactions, Machine learning, ICSR (individual case safety reports), Artificial Intelligence.
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.02.031
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| Download:
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