Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods

This analysis identified two phenotypes based on COVID-19 symptoms and comorbidities. These phenotypes can be accurately recognized using machine learning models, with the AdaBoost model being optimal for predicting in-hospital mortality. The variables "CRP", "IL-2R", "D-dimer", "ST2", "BUN", "NT-proBNP", "neutrophil percentage", and "lymphocyte count" play a significant role in the prediction of subphenotypes. Use the identified subphenotypes for risk stratification in clinical practice. Hyper-inflammatory subphenotypes can be closely monitored, and preventive measures such as early admission to the intensive care unit or prophylactic anticoagulation can be taken.