论文标题
基于证据医学的文本分类的神经语言模型
Neural language models for text classification in evidence-based medicine
论文作者
论文摘要
COVID-19给整个人类带来了重大挑战，但给医学界带来了特别的负担。临床医生必须在无休止的科学文献洪水中不断地对紧急治疗的症状，诊断和有效性进行不断更新。在这种情况下，循证医学（EBM）在策划支持公共卫生和临床实践的最大证据方面的作用是必不可少的，但由于发表的大量研究文章和每天发布的预印刷品而受到挑战。在这种情况下，人工智能可以发挥至关重要的作用。在本文中，我们报告了一个应用研究项目的结果，该项目旨在对科学文章进行分类，以支持Epistemonikos，这是全球指导EBM的最活跃的基金会之一。我们测试了几种基于XLNET神经语言模型的方法，最好的方法将当前的方法平均提高了93 \％，从而节省了从志愿者手动策划Covid-19的研究文章的医生的宝贵时间。
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.
