Pharma R&D Today

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Webinar: Using machine learning to identify adverse events from scientific literature

Posted on February 5th, 2020 by in Pharmacovigilance

The “vigilance” aspect of the pharmacovigilance process can be very challenging. Always being on guard and knowing all of the places to look can be difficult. In a sea of information, it can even seem like a nearly impossible task to maintain awareness of all adverse events (AE). That is why there has been a lot of buzz around technologies that can help automate parts of the pharmacovigilance process.

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Why literature is a valuable source for signal detection: From the EU point of view

Posted on January 9th, 2020 by in Pharmacovigilance

Signal detection is traditionally based on case reporting from healthcare professionals and national regulatory authorities. Yet, there are a number of very notable examples where a safety signal actually came from the scientific literature, such as in the cases of thalidomide, GM-CSF, nifedipine and tamsulosin. (You can learn more about how safety signals detected from a literature report had an impact on the lifecycle of these drugs here.)

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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.

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How Big Data Transforms Reactive Drug Safety to Proactive Pharmacovigilance

Posted on March 7th, 2019 by in Pharma R&D

Adverse drug reactions can be costly and cost is just the tip of the iceberg.  This is highlighted by an FDA website which cites troubling statistics such as the 100k deaths and over 2 million serious adverse drug reactions experienced each year…

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