An interpretable Day-1 machine-learning model for COVID-19 mortality and severity prognosis: development in a Pakistani hospital cohort and external evaluation in Chinese and Italian populations

Early risk stratification of COVID-19 patients at admission informs triage and resource allocation, particularly in low- and middle-income settings where intensive-care capacity is constrained. Most published models are not interpretable, rarely externally validated, and developed almost exclusively in high-income cohorts. We developed an interpretable random-forest classifier for three-class COVID-19 severity (Mild: not ventilated, survived; Severe: ventilated, survived; Fatal: died) using only data available within 24 hours of admission in 321 hospitalised PCR-confirmed COVID-19 patients in

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