DAEDRA: A language model for predicting outcomes in passive pharmacovigilance reporting

arXiv:2402.10951v1 Announce Type: cross
Abstract: Over the recent years, the emergence of large language models (LLMs) has given rise to a proliferation of domain-specific models that are intended to reflect the particularities of linguistic context and content as a correlate of the originating domain. This paper details the conception, design, training and evaluation of DAEDRA, a LLM designed to detect regulatory-relevant outcomes (mortality, ER attendance and hospitalisation) in adverse event reports elicited through passive reporting (PR). While PR is a …

abstract arxiv context cs.cl cs.lg domain emergence language language model language models large language large language models llms paper reporting type