This article shows the ADE Discovery Browser can\u00a0contribute to the visibility of adverse drug events (ADEs) \u2013 and thereby drug safety \u2013 by an\u00a0end-to-end qualitative text mining of ADEs\u00a0associated with a medication like the COVID-19 vaccines.<\/p>\n
The outbreak of a global pandemic revealed the need for quick but also safe development of COVID-19 vaccines. Next to the practical complexity of developing and manufacturing such vaccines, the amount of research accompanying the drug development constituted a challenge by itself. Biomedical literature hosts research findings and case reports of crucial and potential hidden information such as adverse drug events (ADEs) associated with a certain medication consumption. Recently developed COVID-19 vaccines were observed to cause mild ADEs such as fever, redness at the injection site, headache, diarrhoea, muscle pain and also several severe ADEs such as anaphylaxis allergic reactions, thrombocytopenia, bell\u2019s palsy etc. in certain groups of people or individuals. Since the COVID-19 vaccines are being produced and administered, the amount of available literature related to ADEs associated with COVID-19 vaccines have been increasing just like the amount of available biomedical literature in any other topic.<\/p>\n
In order to contribute to the visibility of ADEs and thereby drug safety, our goal was to perform end-to-end qualitative text mining of ADEs associated with a medication from the life sciences literature database, PubMed<\/a>, and to make it possible to interactively discover the ADEs, DRUGs and their causal and semantic relationships. The eventual goal of the ADE Discovery Browser is to provide a system to pharmacovigilance experts where they can easily make use of and evaluate the retrieved information about ADEs from the scientific literature. To achieve that, we performed 2 tasks: named entity recognition (NER) and relation extraction (RE). For each task, we fine-tuned transformers based pre-trained language models (PLM): a generic domain model DistilBERT<\/a> and an in-domain model PubMedBERT<\/a>.<\/p>\n <\/p>\n<\/div>