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

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Manage episode 436241633 series 2749727
Kandungan disediakan oleh Uppsala Monitoring Centre. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Uppsala Monitoring Centre atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

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?

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

Join the conversation on social media
Follow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.
Got a story to share?
We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!
About UMC
Read more about Uppsala Monitoring Centre and how we work to advance medicines safety.

  continue reading

Bab

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)

51 episod

Artwork
iconKongsi
 
Manage episode 436241633 series 2749727
Kandungan disediakan oleh Uppsala Monitoring Centre. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Uppsala Monitoring Centre atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

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?

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

Join the conversation on social media
Follow us on X, LinkedIn, or Facebook and share your thoughts about the show with the hashtag #DrugSafetyMatters.
Got a story to share?
We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!
About UMC
Read more about Uppsala Monitoring Centre and how we work to advance medicines safety.

  continue reading

Bab

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)

51 episod

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