Predicting public Intensive care unit mortality and hospitalization using Data: An evaluation of Brazil’s Largest COVID-19 epidemiological dataset

OBJECTIVES: To assess the SRAG dataset's potential for modeling COVID-19 mortality and LOS-ICU, identify key data gaps, and support the development of predictive tools for ICU planning in future outbreaks. METHODS: for LOS-ICU. Models were tuned to address data imbalance and improve generalizability across the dataset. RESULTS: = 0.35-0.37, indicating a predicted ICU stay of 11.6 ± 6 days and reflecting limited explanatory power due to missing clinical biomarkers. Mortality predictions were robust for triage support, while ICU stay estimates, though less precise, remain useful for operational planning. CONCLUSIONS: This research pioneered the use of a large-scale, highly granular public dataset and leveraged it for mortality prediction in COVID-19 patients in public ICUs. Given the unprecedented scale, academic and managerial improvements were brought to the field, offering generalizable solutions to support ICU triage and early warnings during outbreaks. Addressing the gaps of SRAG could enhance its utility for predictive modeling, especially in LOS-ICU modeling, enabling more accurate results. IMPLICATIONS FOR CLINICAL PRACTICE: Predictive modeling based on real-world ICU data can help healthcare decision-makers anticipate ICU demand, optimize triage, and allocate critical care resources with greater equity and timeliness during future epidemiological crises, ultimately improving patient outcomes.