Lau Wilson, Payne Thomas H, Uzuner Ozlem, Yetisgen Meliha
Department of Biomedical and Health Informatics, University of Washington, Seattle, WA.
School of Medicine, University of Washington, Seattle, WA.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:335-344. eCollection 2020.
Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 1367 radiology reports annotated for recommendation information. Our extraction models achieved 0.93 f-score for recommendation sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for time frame. We applied the extraction models to a set of over 3.3 million radiology reports and analyzed the adherence of follow-up recommendations.
当影像学研究发现异常时，后续建议的传达容易出错。在本文中，我们提出了一种基于深度学习的自然语言处理方法，以自动识别放射学报告中具有临床重要性的建议。我们的方法首先识别建议句子，然后提取已识别建议的原因、检查和时间框架。为了训练我们的提取模型，我们创建了一个包含1367份标注了建议信息的放射学报告的语料库。我们的提取模型在建议句子方面的F值为0.93，在原因方面的F值为0.65，在检查方面的F值为0.73，在时间框架方面的F值为0.84。我们将提取模型应用于一组超过330万份放射学报告，并分析了后续建议的依从性。
