Uppsala Reports Long Reads – Found in space

Send a text message to the show!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 termsWhat advantages the new method offers over MedDRA classificationsWhich pharmacovigilance tasks could benefit from this new researchWant 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.Got a story to share?We’re always looking for new topics and interesting voices. If you have an idea or any other feedback for the show, get in touch!About UMCUppsala Monitoring Centre promotes safer use of medicines and vaccines for everyone everywhere. Follow us on Facebook, LinkedIn, X, and Bluesky.