  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.
