Utilization of Machine Learning Approaches to Predict Mortality in Pediatric Warzone Casualties.

BACKGROUND Identification of pediatric trauma patients at the highest risk for death may promote optimization of care. This becomes increasingly important in austere settings with constrained medical capabilities. This study aimed to develop and validate predictive models using supervised machine learning (ML) techniques to identify pediatric warzone trauma patients at the highest risk for mortality. METHODS Supervised learning approaches using logistic regression (LR), support vector machine (SVM), neural network (NN), and random forest (RF) models were generated from the Department of Defense Trauma Registry, 2008-2016. Models were tested and compared to determine the optimal algorithm for mortality. RESULTS A total of 2,007 patients (79% male, median age range 7-12 years old, 62.5% sustaining penetrating injury) met the inclusion criteria. Severe injury (Injury Severity Score > 15) was noted in 32.4% of patients, while overall mortality was 7.13%. The RF and SVM models displayed recall values of .9507 and .9150, while LR and NN displayed values of .8912 and .8895, respectively. Random forest (RF) outperformed LR, SVM, and NN on receiver operating curve (ROC) analysis demonstrating an area under the ROC of .9752 versus .9252, .9383, and .8748, respectively. CONCLUSION Machine learning (ML) techniques may prove useful in identifying those at the highest risk for mortality within pediatric trauma patients from combat zones. Incorporation of advanced computational algorithms should be further explored to optimize and supplement the diagnostic and therapeutic decision-making process.

背景 识别死亡风险最高的儿童创伤患者可能有助于优化治疗。在医疗能力受限的艰苦环境中，这一点变得愈发重要。本研究旨在利用有监督的机器学习（ML）技术开发并验证预测模型，以识别战区儿童创伤患者中死亡风险最高的人群。 方法 利用逻辑回归（LR）、支持向量机（SVM）、神经网络（NN）和随机森林（RF）模型的有监督学习方法是从2008 - 2016年美国国防部创伤登记处的数据中生成的。对这些模型进行了测试和比较，以确定用于预测死亡率的最佳算法。 结果 共有2007名患者符合纳入标准（79%为男性，年龄中位数范围为7 - 12岁，62.5%遭受穿透性损伤）。32.4%的患者有严重损伤（损伤严重程度评分＞15），而总体死亡率为7.13%。随机森林（RF）和支持向量机（SVM）模型的召回值分别为0.9507和0.9150，而逻辑回归（LR）和神经网络（NN）的值分别为0.8912和0.8895。在受试者工作特征曲线（ROC）分析中，随机森林（RF）优于LR、SVM和NN，其ROC曲线下面积分别为0.9752，而LR、SVM和NN分别为0.9252、0.9383和0.8748。 结论 机器学习（ML）技术可能有助于识别战区儿童创伤患者中死亡风险最高的人群。应进一步探索纳入先进的计算算法，以优化和补充诊断及治疗决策过程。