Home health remedies Elsevier uses machine learning to benefit pharmacovigilance

Elsevier uses machine learning to benefit pharmacovigilance

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Posted on November 7th, 2019 by in Pharmacovigilance

Monitoring the scientific literature for adverse drug
reactions (ADRs) is critical to maintaining drug safety, and there is no room
for error. As regulations tighten, pharmacovigilance teams are seeking better
strategies and methods for ensuring that all ADRs are identified in the most
effective and efficient way possible.

Elsevier is providing leadership in this area in many ways, including efforts to use AI and natural language processing (NLP) technology to monitor the literature. Umesh Nandal, an Elsevier scientist who specializes in machine learning and NLP, and his team has been doing great work on the automated extraction of ADRs from biomedical literature and FDA drug labels. As a part of our outreach to the global pharmacovigilance community, I will be presenting on this work at the China Pharmacovigilance Conference in Guangzhou this month.

The presentation, which touches on the future of AI and how
Elsevier is utilizing machine learning with its various information solutions,
will focus on explaining various approaches for extracting ADRs and highlight a
few particular projects where Elsevier is applying AI.

We are excited to be at the forefront of these developments
in the use of state-of-the-art technology to meet the needs of
pharmacovigilance professionals.

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We’re happy to discuss your needs and show you how Elsevier’s Solution can help.

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