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Elsevier uses machine learning to benefit pharmacovigilance

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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