A Natural Language Processing Approach Towards Harmonized Communication of Uncertainties Identified During the...

Within the European Union, the European Medicines Agency's (EMA's) European Public Assessment Report (EPAR) is an important source of information for healthcare professionals and patients that allows them to understand important risks and uncertainties associated with the use of a medicine. However, the EPAR sections describing such important uncertainties can differ substantially in wording, length, and detail, thereby potentially limiting understanding. In this study, we therefore present a natural language processing approach to cluster sentences extracted from the sections on uncertainties in EPARs of centrally authorized medicines, as a steppingstone to harmonization of text describing uncertainties. We used a BERT language model together with dimensionality reduction (Uniform Manifold Approximation and Projection (UMAP)) and clustering (Density-Based Spatial Clustering of Applications with Noise (DBSCAN)) to identify semantic similarities between sentences. Clusters were labeled according to an overarching topic by reviewing the semantically similar sentences. Each cluster was also characterized according to medicine-related characteristics, such as efficacy or side effects. In total, 1,648 medicines were included in this study. For 573 of these medicines (authorized July 27, 2010 to December 31, 2022), we identified an EPAR that described a complete regulatory dossier and contained sections on uncertainties. Of these, 553 EPARs could be attributed to unique active substance-indication combinations. In these 553 EPARs, we identified 13,105 sentences in sections on uncertainties, leading to 26 clusters of which 2 were labeled as noise. The clusters and associated topics provided in this article can be used by regulators and medicine developers as a steppingstone toward a unified way of communicating uncertainties identified during the EMA process to the broader public.