Abstract
The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) to analyze large datasets has been crucial for rapidly identifying relevant biomarkers for disease prognosis, including COVID-19. This approach enhances diagnostics in emergency settings, allowing for more accurate and efficient patient management. This study demonstrates how machine learning algorithms in emergency departments can rapidly identify key biomarkers for the vital prognosis in an emerging pandemic using COVID-19 as an example by analyzing clinical, epidemiological, analytical, and radiological data. All consecutively admitted patients were included, and more than 89 variables were processed using the Random Forest (RF) algorithm. The RF model achieved the highest balanced accuracy at 92.61%. The biomarkers most predictive of mortality included procalcitonin (PCT), lactate dehydrogenase (LDH), and C-reactive protein (CRP). Additionally, the system highlighted the significance of interstitial infiltrates in chest X-rays and D-dimer levels. Our results demonstrate that RF is crucial in identifying critical biomarkers in emerging diseases, accelerating data analysis, and optimizing prognosis and personalized treatment, emphasizing the importance of PCT and LDH in high-risk patients.