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

Drug Safety Matters

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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Kapitel

1. Uppsala Reports Long Reads – Weeding out duplicates to better detect side effects (00:00:00)

2. Intro (00:00:09)

3. Article read (00:01:12)

4. Welcome, Jim! (00:05:57)

5. Definitions of AI, machine learning and algorithms (00:07:19)

6. How do we develop algorithms at UMC? (00:08:47)

7. Evaluating the performance of algorithms (00:10:13)

8. How many algorithms has UMC developed? (00:13:09)

9. How many duplicates in VigiBase? (00:13:45)

10. Which duplicate to keep, and which to weed out? (00:15:29)

11. Other examples of algorithms used in pharmacovigilance (00:18:10)

12. Where will we be in a couple of years? (00:20:18)

13. Pitfalls to be mindful of, heading into the future (00:21:47)

14. A dream algorithm? (00:22:32)

15. Thank you and goodbye (00:23:56)

16. Outro (00:24:09)