The fatality rate can be dramatically reduced with the help of emergency medical services. The purpose of this study was to establish a computational algorithm to predict the injury severity, so as to improve the timeliness, appropriateness, and efficacy of medical care provided. The computer simulations of full-frontal crashes with rigid wall were carried out using LS-DYNA and MADYMO under different collision speeds, airbag deployment time, and seatbelt wearing condition, in which a total of 84 times simulation was conducted. Then an artificial neural network is adopted to construct relevance between head and chest injuries and the injury risk factors; 37 accident cases with Event Data Recorder data and information on occupant injury were collected to validate the model accuracy through receiver operating characteristic analysis. The results showed that delta-v, seatbelt wearing condition, and airbag deployment time were important factors in the occupant's head and chest injuries. When delta-v increased, the occupant had significantly higher level of severe injury on the head and chest; there is a significant difference of Head Injury Criterion and Combined Thoracic Index whether the occupant wore seatbelt. When the airbag deployment time was less than 20 ms, the severity of head and chest injuries did not significantly vary with the increase of deployment time. However, when the deployment time exceeded 20 ms, the severity of head and chest injuries significantly increased with increase in deployment time. The validation result of the algorithm showed that area under the curve = 0.747, p < 0.05, indicating a medium level of accuracy, nearly to previous model. The computer simulation and artificial neural network have a great potential for developing injury risk estimation algorithms suitable for Advanced Automatic Crash Notification applications, which could assist in medical decision-making and medical care.

中文翻译：

---

通过结合计算机模拟和真实碰撞，对涉及正面碰撞的乘员进行初步伤害风险评估。

在紧急医疗服务的帮助下，死亡率可以大大降低。本研究的目的是建立一种计算算法来预测伤害严重程度，从而提高医疗护理的及时性、适当性和有效性。利用LS-DYNA和MADYMO对不同碰撞速度、安全气囊展开时间和安全带佩戴情况下的正面刚性墙碰撞进行计算机模拟，共模拟84次。然后采用人工神经网络构建头部和胸部损伤与损伤危险因素之间的相关性；收集了 37 个事故案例的事件数据记录器数据和乘员伤害信息，通过接收器操作特性分析来验证模型的准确性。结果表明，Delta-V、安全带佩戴情况和安全气囊展开时间是导致乘员头部和胸部受伤的重要因素。当delta-v增加时，乘员头部和胸部的严重伤害程度显着升高；乘员是否系安全带，头部伤害标准和胸部综合指数存在显着差异。当安全气囊展开时间小于20 ms时，头部和胸部伤害的严重程度随着展开时间的增加没有显着变化。然而，当部署时间超过20毫秒时，头部和胸部损伤的严重程度随着部署时间的增加而显着增加。算法验证结果显示，曲线下面积 = 0.747，p < 0.05，表明精度处于中等水平，接近之前的模型。计算机模拟和人工神经网络在开发适用于高级自动碰撞通知应用的伤害风险估计算法方面具有巨大潜力，可以协助医疗决策和医疗护理。