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

Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework