Per the challenge, “OSE is interested in a tool that would enable pharmacovigilance safety evaluators to automate the identification of labeled AEs which could facilitate triage, review and processing of safety case reports.” Basically, they want to know what is ‘known’ on the product labels, so that they can only flag ‘unknown’ signals that arise in their signal detection and clinical adjudication phase. I know this is a shared interest at Uppsala Monitoring Centre, and of interest to various safety organizations within the pharmaceutical industry.
Given the work by @jon_duke on SPLICER, @rkboyce on LAERTES, @ericaVoss on the common evidence model, @Chunhua_Weng’s lab in parsing CT.gov, as well as the expertise within the NLP workgroup (@HuaXu @noemie @nigam ) , this seems like a great opportunity for the OHDSI community to showcase its expertise and collaborate as a group to make an important contribution.
They’ve made a reference dataset available for training your model, which you can download once you register on their site now. It appears submissions will be due end of January, based on a testset that will be released at a later time.
If anyone is interested in collaborating, we can use this forum threads to keep the conversation going.