Amr Ellabban
speaker
114 appearances
2 recordings
1 series
first heard Nov 2021
last heard Feb 2023
Amr Ellabban’s voice in public audio — every appearance, attributed to the second.
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That's really interesting stuff.
And maybe an on a slightly different note, I mean the last few years have been very disruptive for people all over the world, but really thrown the spotlight on pharma companies, as you say, especially around the the global vaccine rollout.
What have been some of the major changes that you've seen during this time?
And what does this mean for pharma and healthcare more widely?
Is this increased flexibility and collaboration a one off in response to a a global emergency, or is this a fundamental change in the modus operandi of both pharma and the regulators?
And speaking as someone who's run plenty of statistical tests in in recent months, I can I can vouch for the fact that it it does take time to collect the data.
I guess before we go into how evaluators supported pharma throughout this journey, can you tell us a little bit more about what you do?
That is a
You alluded to the importance of well curated data earlier.
Can you tell me a bit more about that?
Very true.
And we've we've seen in lots of different fields how painful it is to work with dirty data when doing data science.
And maybe coming back to some of those predictive models that you mentioned, and maybe a bit of a leading question since uh I know we we collaborated a bit on that piece of work a few years ago.
But how did you go about prioritizing those and building them?
likelihood of success.
Sound like some really interesting data points to work with.
And how did how did you go about building those models and picking the right attributes to use?
That makes a lot of sense and it's very true of what we've seen elsewhere, that it's that partnership of data science and the domain expertise that makes these kinds of products a real success.
And I mean, did you have to make any s major decisions during the build up
Really avoiding going down the the rabbit hole of black box machine learning.
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