Neural language models for text classification in evidence-based medicine
This work provides a strong specific gain for physicians and EBM foundations by significantly automating the classification of scientific literature, thereby saving valuable curation time.
This paper addresses the challenge of classifying scientific articles for evidence-based medicine (EBM) curation, particularly in the context of COVID-19. The authors developed a neural language model-based approach, with the best model (XLNet) achieving a 93% improvement in average F1-score compared to the existing method.
The COVID-19 has brought about a significant challenge to the whole of humanity, but with a special burden upon the medical community. Clinicians must keep updated continuously about symptoms, diagnoses, and effectiveness of emergent treatments under a never-ending flood of scientific literature. In this context, the role of evidence-based medicine (EBM) for curating the most substantial evidence to support public health and clinical practice turns essential but is being challenged as never before due to the high volume of research articles published and pre-prints posted daily. Artificial Intelligence can have a crucial role in this situation. In this article, we report the results of an applied research project to classify scientific articles to support Epistemonikos, one of the most active foundations worldwide conducting EBM. We test several methods, and the best one, based on the XLNet neural language model, improves the current approach by 93\% on average F1-score, saving valuable time from physicians who volunteer to curate COVID-19 research articles manually.