  The COVID-19 pandemic continues to have a devastating global impact, and has
placed a tremendous burden on struggling healthcare systems around the world.
Given the limited resources, accurate patient triaging and care planning is
critical in the fight against COVID-19, and one crucial task within care
planning is determining if a patient should be admitted to a hospital's
intensive care unit (ICU). Motivated by the need for transparent and
trustworthy ICU admission clinical decision support, we introduce COVID-Net
Clinical ICU, a neural network for ICU admission prediction based on patient
clinical data. Driven by a transparent, trust-centric methodology, the proposed
COVID-Net Clinical ICU was built using a clinical dataset from Hospital
Sirio-Libanes comprising of 1,925 COVID-19 patient records, and is able to
predict when a COVID-19 positive patient would require ICU admission with an
accuracy of 96.9% to facilitate better care planning for hospitals amidst the
on-going pandemic. We conducted system-level insight discovery using a
quantitative explainability strategy to study the decision-making impact of
different clinical features and gain actionable insights for enhancing
predictive performance. We further leveraged a suite of trust quantification
metrics to gain deeper insights into the trustworthiness of COVID-Net Clinical
ICU. By digging deeper into when and why clinical predictive models makes
certain decisions, we can uncover key factors in decision making for critical
clinical decision support tasks such as ICU admission prediction and identify
the situations under which clinical predictive models can be trusted for
greater accountability.
