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 Umesh Nandal PhD 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.
For instance, there was recently a test done on the feasibility of using AI and robotic process automation to automate the processing of AE reports. As the study’s authors wrote in Clinical Pharmacology and Therapeutics, “The result confirmed the feasibility of using artificial intelligence–based technology to support extraction from adverse event source documents and evaluation of case validity.”
The advantages of automation in pharmacovigilance
There are a number of potential benefits to using AI for real-time monitoring of the literature for adverse events, including:
- Pharmacovigilance teams can be immediately alerted when an AE appears in the literature, ensuring quick action
- Acting on AE reports as soon as possible can lessen the potential negative impact on other patients, and also helps the organization to maintain the highest safety standards
- The automated tools can also extract relevant information about the adverse events, such as what type of event it was, which drug was involved, information about the patient, etc.
- An AI-driven system can also make it easier for pharmacovigilance professionals to manage literature searches by automatically creating records of which searches have been done and what articles have been reviewed for AEs
Learn more in our webinar
In an upcoming webinar, which will be held on February 19 at 10AM EST, I will be discussing the progress that Elsevier has been making in using AI, machine learning and natural language processing to identify adverse events in the biomedical literature, which can save companies a significant amount of time and money. Topics in the webinar will include the challenges of using AI to mine literature, how to create a quality training set for machine learning and much more.
Register here to take part in this free webinar.
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Umesh Nandal PhD
Principal NLP Scientist
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