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Chest radiography has been a recommended procedure for patient triaging andresource management in intensive care units (ICUs) throughout the COVID-19pandemic. The machine learning efforts to augment this workflow have been longchallenged due to deficiencies in reporting, model evaluation, and failure modeanalysis. To address some of those shortcomings, we model radiological featureswith a human-interpretable class hierarchy that aligns with the radiologicaldecision process. Also, we propose the use of a data-driven error analysismethodology to uncover the blind spots of our model, providing furthertransparency on its clinical utility. For example, our experiments show thatmodel failures highly correlate with ICU imaging conditions and with theinherent difficulty in distinguishing certain types of radiological features.Also, our hierarchical interpretation and analysis facilitates the comparisonwith respect to radiologists' findings and inter-variability, which in returnhelps us to better assess the clinical applicability of models.

Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs