BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance.

ABSTRACT: Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether a report is of serious nature based solely on the free-text fields and adverse event terms in the report, potentially allowing reports mislabelled at time of reporting to be detected and prioritized for assessment. We consider four different NLP models at various levels of complexity, bootstrap their train-validation data split to eliminate random effects in the performance estimates and conduct

SUBMITTER: Bergman E 

PROVIDER: S-EPMC10699587 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature