Sang Won Park, Na Young Yeo, Seonguk Kang, Taejun Ha, Tae-Hoon Kim, DooHee Lee, Dowon Kim, Seheon Choi, Minkyu Kim, DongHoon Lee, DoHyeon Kim, Woo Jin Kim, Seung-Joon Lee, Yeon-Jeong Heo, Da Hye Moon, Seon-Sook Han, Yoon Kim, Hyun-Soo Choi, Dong Kyu Oh, Su Yeon Lee, MiHyeon Park, Chae-Man Lim, Jeongwon Heo
BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department...
February 5, 2024: Journal of Korean Medical Science