ICU admission medical triage of COVID19 patients using Artificial Intelligence

The advent of the SARS-CoV-2 virus precipitated a global pandemic, ushering in profound challenges for public health worldwide. Within this dynamic landscape, the realm of COVID-19 patient management and the ability to accurately predict patient outcomes remain marked by the absence of standardized protocols. To address this critical gap, our study endeavors to construct a robust predictive model specifically designed to forecast the likelihood of Intensive Care Unit (ICU) admission and mortality among COVID-19 patients who have undergone prior hospitalization.Employing sophisticated decision tree algorithms, we meticulously examined an expansive dataset drawn from patients’ clinical histories. Notably, our model exhibited a commendable 80% accuracy rate in predicting ICU admissions and an even more impressive 94% accuracy rate for forecasting mortality—exceeding the performance of alternative methodologies.The implications of our research extend far beyond the confines of academia, with tangible benefits for healthcare professionals and policymakers alike. By elucidating the predictive variables influencing patient outcomes, our work provides invaluable guidance for optimizing resource allocation and enhancing patient care during the pandemic. Nevertheless, we acknowledge the imperative of ongoing refinement and validation to ensure the practical applicability of our model within real-world clinical settings. As the COVID-19 crisis continues to evolve, our research serves as a beacon of hope and a foundation for evidence-based decision-making in the pursuit of improved patient outcomes and enhanced public health preparedness.