Uppsala Reports Long Reads – Weeding out duplicates to better detect side effects

Duplicate reports are a big problem when it comes to signal
detection, but with the help of machine learning and new ways of
comparing reports, we may more effectively detect them. 


This episode is part of the Uppsala Reports Long Reads series –
the most topical stories from UMC’s pharmacovigilance news site,
brought to you in audio format. Find the original article here.


After the read, we speak to author Jim Barrett, Senior Data
Scientist at UMC, to learn more about the duplicate detection
algorithm and UMC’s work to develop AI resources for
pharmacovigilance.



Tune in to find out:


How the new algorithm handles duplicates in VigiBase

About different approaches for developing algorithms

Why it can be challenging to evaluate the performance of an
algorithm




Want to know more?


Listen to the Drug Safety Matters interview with Michael
Glaser about his Uppsala Reports article “Ensuring trust in AI/ML
when used in pharmacovigilance” and check out the episode’s
extensive list of links for more on AI in
pharmacovigilance. 

Artificial intelligence in pharmacovigilance – value
proposition and the need for critical appraisal, a presentation
by Niklas Norén, Head of Research at UMC, given at University of
Verona in April 2024.  



Finally, don’t forget to ­subscribe to the monthly Uppsala
Reports newsletter for free regular updates from the world of
pharmacovigilance.





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