Hospitalization for violent trauma costs as much as $8.5 billion per year in the United States, and reinjury rates are as high as 40%.¹ Most trauma reinjury studies include data from a single center. However, nearly 60% of reinjured patients present to a different hospital, making prediction of reinjury difficult.^1,2 Machine learning allows computers to learn from iterations without programming. Machine learning is highly accurate for predicting surgical outcomes³ and suicide risk⁴ but, to our knowledge, has never been used in trauma outcomes. The purpose of this study was to compare several machine learning models for prediction of reinjury after penetrating trauma.

在美国，因暴力创伤而住院的费用每年高达85亿美元，再受伤率高达40％。¹大多数创伤再损伤研究都包括来自单个中心的数据。但是，将近60％的再伤患者在另一所医院就诊，因此很难预测再伤。^{1 ，2}机器学习让计算机从迭代学习无需编程。机器学习对于预测手术结局³和自杀风险⁴是高度准确的，但据我们所知，从未用于创伤结局。这项研究的目的是比较几种机器学习模型，以预测穿透性创伤后的再损伤。