Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning

ConclusionsNinety-one VAERS reports were annotated, resulting in 2512 entities. The corpus was made publicly available to promote community efforts on vaccine AEs identification. Deep learning-based methods (eg, bi-long short-term memory and BERT models) outperformed conventional machine learning-based methods (ie, conditional random fields with extensive features). The BioBERT large model achieved the highest exact match F-1 scores onnervous_AE,procedure,social_circumstance, andtemporal_expression; while VAERS BERT large models achieved the highest exact match F-1 scores oninvestigation andother_AE. An ensemble of these 2 models achieved the highest exact match microaveraged F-1 score at 0.6802 and the second highest lenient match microaveraged F-1 score at 0.8078 among peer models.
Source: Journal of the American Medical Informatics Association - Category: Information Technology Source Type: research