Uppsala Reports Long Reads – Found in space

When reporting adverse reactions to drugs, people can choose from
a plethora of different terms to describe their experience. But
that makes it difficult and time-consuming for analysts to tell
how similar two case safety reports are. A new method developed
by UMC data scientist Lucie Gattepaille comes to the
rescue.

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

After the read, Uppsala Reports editor Gerard Ross interviews
Lucie on her work behind the scenes and the broader implications
of her research for the pharmacovigilance field.

Tune in to find out:


How natural language processing can help connect related drug
and adverse reaction terms

What advantages the new method offers over MedDRA
classifications

Which pharmacovigilance tasks could benefit from this new
research



Want to know more?Lucie presented her work on vector
representations for pharmacovigilance at the IEEE International
Conference on Healthcare Informatics in 2019. And here’s some
background reading on distributed representations of words and
phrases.





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