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The COVID-19 pandemic continues to have a devastating global impact, and hasplaced a tremendous burden on struggling healthcare systems around the world.Given the limited resources, accurate patient triaging and care planning iscritical in the fight against COVID-19, and one crucial task within careplanning is determining if a patient should be admitted to a hospital'sintensive care unit (ICU). Motivated by the need for transparent andtrustworthy ICU admission clinical decision support, we introduce COVID-NetClinical ICU, a neural network for ICU admission prediction based on patientclinical data. Driven by a transparent, trust-centric methodology, the proposedCOVID-Net Clinical ICU was built using a clinical dataset from HospitalSirio-Libanes comprising of 1,925 COVID-19 patient records, and is able topredict when a COVID-19 positive patient would require ICU admission with anaccuracy of 96.9% to facilitate better care planning for hospitals amidst theon-going pandemic. We conducted system-level insight discovery using aquantitative explainability strategy to study the decision-making impact ofdifferent clinical features and gain actionable insights for enhancingpredictive performance. We further leveraged a suite of trust quantificationmetrics to gain deeper insights into the trustworthiness of COVID-Net ClinicalICU. By digging deeper into when and why clinical predictive models makescertain decisions, we can uncover key factors in decision making for criticalclinical decision support tasks such as ICU admission prediction and identifythe situations under which clinical predictive models can be trusted forgreater accountability.

COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification