Acute respiratory distress syndrome (ARDS) is a life-threatening condition that is often undiagnosed or diagnosed late. ARDS is especially prominent in those infected with COVID-19. We explore the automatic identification of ARDS indicators and confounding factors in free-text chest radiograph reports. We present a new annotated corpus of chest radiograph reports and introduce the Hierarchical Attention Network with Sentence Objectives (HANSO) text classification framework. HANSO utilizes fine-grained annotations to improve document classification performance. HANSO can extract ARDS-related information with high performance by leveraging relation annotations, even if the annotated spans are noisy. Using annotated chest radiograph images as a gold standard, HANSO identifies bilateral infiltrates, an indicator of ARDS, in chest radiograph reports with performance (0.87 F1) comparable to human annotations (0.84 F1). This algorithm could facilitate more efficient and expeditious identification of ARDS by clinicians and researchers and contribute to the development of new therapies to improve patient care.

翻译：急性呼吸困难综合症(ARDS)是一种威胁生命的状况,往往未经诊断或诊断为晚期。在感染COVID-19的人中,ARDS特别突出。我们探讨自动识别ARDS指标和自由文本胸腔放射报告中的混杂因素。我们提出了一套新的附加说明的胸腔射线报告,并介绍了带有判决目标的分级框架。HANSCO利用细微的注解来提高文件分类性能。HANSO可以通过利用关系说明来提取ARDS相关信息,并具有很高的性能,即使附加说明的间隔是吵闹的。HANNSO使用附加说明的胸腔射线图像作为黄金标准,在胸腔射线报告中确定双边渗透,ARDS的指标(0.87 F1),其性能与人文说明相当(0.84 F1)。这种算法可以促进临床医生和研究人员更高效、更迅速地识别ARDS,并有助于开发新的疗法,以改善病人的护理。