MACHINE LEARNING IN THE MANAGEMENT OF SURGICAL SEPSIS IN COMBAT TRAUMA

Sepsis remains a leading cause of morbidity and mortality among combat casualty survivors who have survived their initial battlefield injuries. The combination of massive tissue damage, hemorrhagic shock, and surgical contamination triggers an explosive, dysregulated inflammatory response that often masks the standard clinical signs of infection. Traditional scoring systems frequently fail to account for the complex, time-dependent pathophysiology unique to combat -related surgical sepsis. This study explores the integration of machine learning (ML) algorithms into the clinical management of combat -related surgical sepsis, focusing on the identification of clinical subphenotypes via cluster analysis to advance precision medicine on the battlefield and in Role 3–4 military hospitals.