A vehicle occupant injury prediction algorithm based on road crash and emergency medical data

IF 3.9 2区 工程技术 Q1 ERGONOMICS Journal of Safety Research Pub Date : 2024-10-24 DOI:10.1016/j.jsr.2024.09.015
Tetsuya Nishimoto , Kazuhiro Kubota , Giulio Ponte
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Abstract

Introduction: Advanced Automatic Collision Notification (AACN) systems are an automobile safety technology designed to reduce the number of fatalities in traffic accidents by optimizing early treatment methods. AACN systems rely on robust injury prediction algorithms, however, despite the importance of time to treatment, current injury prediction algorithms used in AACN systems do not take this critical time period time into consideration. Method: This study developed a vehicle occupant injury prediction algorithm by using emergency transport time in addition to mass crash data, to determine the risk of serious injury for vehicle occupants in a road crash. Two sources of de-identified data were used: The South Australian Traffic Accident Reporting System (TARS) database and the highly detailed South Australian Serious Injury Database (SID). Firstly, the TARS data, a large statistical crash dataset, was imputed into a logistic regression analysis to produce a base injury prediction algorithm. The important effect of emergency transport time on the risk of death and serious injury was then independently quantified as an odds ratio (OR) from the SID. The ORs were converted into regression coefficients and subsequently introduced into the base injury prediction algorithm to produce an enhanced injury prediction algorithm. Results: The ORs calculated from the SID showed that the risk of death and serious injury increased with increasing transport time: 61–90 min (OR = 1.6), 91–120 min (OR = 3.3), and > 120 min (OR = 4.9), compared to a transport time of 60 min or less. An assessment of the base algorithm compared to the enhanced injury prediction algorithm through Receiver Operating Characteristic (ROC) analysis, demonstrated a prediction accuracy improvement from AUC 0.70 to AUC 0.73 when evaluating the respective algorithms. The injury prediction calculations indicate that the impact of two risk factors, transport time and age-related decline in human injury tolerance, are significant, and both have a strong influence on the increased risk of serious injury. Conclusions: The impact of emergency transport time on the risk of fatal and serious injuries was determined from a relatively small, but data rich SID. Subsequently this was incorporated into an injury prediction algorithm constructed from the large (TARS) statistical crash data set to produce an enhanced injury prediction algorithm. Practical Application: By adding the effect of transport time to enhance the basic injury prediction algorithm, an AACN that incorporates such an algorithm can be used to determine the probability of death or serious injury due to delayed treatment. Further, such a system can be used to improve policies and procedures to optimize emergency transport time.
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基于道路碰撞和紧急医疗数据的车辆乘员伤害预测算法
引言高级自动碰撞通知(AACN)系统是一项汽车安全技术,旨在通过优化早期治疗方法来减少交通事故中的死亡人数。AACN 系统依赖于强大的伤害预测算法,然而,尽管治疗时间非常重要,但目前 AACN 系统中使用的伤害预测算法并未将这一关键时间段考虑在内。方法:本研究开发了一种车辆乘员伤害预测算法,除大量碰撞数据外,还使用了紧急运送时间,以确定道路碰撞中车辆乘员受重伤的风险。研究使用了两个来源的去标识化数据:南澳大利亚交通事故报告系统(TARS)数据库和高度详细的南澳大利亚重伤数据库(SID)。首先,将 TARS 数据(一个大型统计车祸数据集)归入逻辑回归分析,以生成基本伤害预测算法。然后,根据 SID 数据,将紧急交通时间对死亡和重伤风险的重要影响独立量化为几率比(OR)。将几率比转换为回归系数,然后将其引入基础伤害预测算法,从而生成增强型伤害预测算法。结果:根据 SID 计算出的 OR 显示,死亡和重伤风险随着运送时间的增加而增加:与 60 分钟或更短的运送时间相比,61-90 分钟(OR = 1.6)、91-120 分钟(OR = 3.3)和 > 120 分钟(OR = 4.9)。通过接收方操作特征(ROC)分析,对基础算法和增强型伤害预测算法进行了评估,结果表明,在对各自算法进行评估时,预测准确率从 AUC 0.70 提高到 AUC 0.73。伤害预测计算结果表明,运输时间和与年龄相关的人体伤害耐受力下降这两个风险因素的影响非常显著,而且都对严重伤害风险的增加有很大影响。结论:紧急运送时间对致命和严重伤害风险的影响是通过规模相对较小但数据丰富的 SID 确定的。随后,我们将这一结果纳入了从大型(TARS)统计碰撞数据集中构建的伤害预测算法中,从而产生了一种增强型伤害预测算法。实际应用:通过添加运输时间的影响来增强基本的伤害预测算法,采用这种算法的 AACN 可用于确定因延误治疗而导致死亡或重伤的概率。此外,这种系统还可用于改进政策和程序,以优化紧急运送时间。
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来源期刊
CiteScore
6.40
自引率
4.90%
发文量
174
审稿时长
61 days
期刊介绍: Journal of Safety Research is an interdisciplinary publication that provides for the exchange of ideas and scientific evidence capturing studies through research in all areas of safety and health, including traffic, workplace, home, and community. This forum invites research using rigorous methodologies, encourages translational research, and engages the global scientific community through various partnerships (e.g., this outreach includes highlighting some of the latest findings from the U.S. Centers for Disease Control and Prevention).
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