:,

Courtney H Meyer1, Jonathan Nguyen, Andrew ElHabr

  • 1From the Department of Trauma and Surgical Critical Care (C.H.M., J.N., T.S., J.G., J.D.S., J.S., C.D., C.N., R.N.S.), Grady Health System; Department of Surgery (C.H.M., T.S., J.G., J.D.S., J.S., C.D., J.L., C.M.C., R.N.S.), Emory University School of Medicine; Department of Behavioral, Social and Health Sciences (C.H.M., R.N.S.), Rollins School of Public Health, Emory University; Department of Surgery (J.N.), Morehouse School of Medicine; Department of Operations Research (A.E.), Georgia Institute of Technology, Atlanta, Georgia; Department of Biomedical Engineering (N.V.), University of Texas at Austin, Austin, Texas; and Department of Surgery and Emory Critical Care Center (J.L., C.M.C.), Emory University School of Medicine, Atlanta, Georgia.

概括

创伤患者的超大规模输血 (UMT) 决策是复杂的. 机器学习模型使用特定时间的数据,而不仅仅是单位计数,可以预测死亡率和指导复苏,改善患者的治疗结果.