Abstract：
Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. The primary endpoint was mortality, and the secondary endpoint was requirement for chronic care. Results: Data of 5871 patients were collected. We then used the eXtreme Gradient Boosting (xGBT) machine learning model to create two algorithms: a complete model and a short-term model. The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.
摘要：
背景和目的：我们开发了一种机器学习算法来分析与创伤相关的数据，并预测创伤患者的死亡率和慢性护理需求。材料与方法：收集2015年至2016年收治的创伤患者的临床资料。然后，我们对该数据库进行了不同的机器学习技术，并通过交叉验证选择了准确性最高的技术。主要终点是死亡率，次要终点是需要慢性护理.结果：收集了5871例患者的数据。然后，我们使用极限梯度提升（xGBT）机器学习模型创建了两种算法：完整模型和短期模型。完整的模型显示了86%的恢复召回率，30%用于慢性护理，死亡率为67%，80%为并发症；适合ED的短期模型显示89%的康复召回率，25%用于慢性护理，死亡率为41%。结论：我们开发了一种机器学习算法，该算法对健康康复组显示出良好的回忆，但对需要长期护理或有死亡风险的患者则显示出不令人满意的结果。该算法的预测能力可以通过实现诸如年龄组分类、严重性选择,和创伤相关变量的评分校准。
