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.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn, 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 UMCRead more about Uppsala Monitoring Centre and how we work to make medicines safer for patients.