Alrawashdeh Ahmad, Alqahtani Saeed, Alkhatib Zaid I, Kheirallah Khalid, Melhem Nebras Y, Alwidyan Mahmoud, Al-Dekah Arwa M, Alshammari Talal, Nehme Ziad
Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan.
Department of Emergency Medical Services, Prince Sultan Military College for Health Sciences, Dhahran, Saudi Arabia.
Prehosp Disaster Med. 2024 Oct;39(5):368-378. doi: 10.1017/S1049023X24000414. Epub 2024 May 17.
The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS).
Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains.
This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms.
Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.
本研究旨在总结机器学习（ML）的应用及其在紧急医疗服务（EMS）中的表现的相关文献。
检索了四个相关电子数据库（从创建到2024年1月），查找所有采用EMS指导的ML算法来提高EMS临床和运营表现的原始研究。两名评审员筛选检索到的研究，并从纳入研究中提取相关数据。在主要领域和子领域中定量描述了纳入研究的特征、所采用的ML算法及其表现。
本综述共纳入了2005年至2024年发表的164项研究。其中，125项聚焦于临床领域，39项聚焦于运营领域。ML算法的特征，如样本量、输入特征的数量和类型以及表现，在应用的领域和子领域之间及内部有所不同。ML算法的临床应用包括分诊或诊断分类（n = 62）、治疗预测（n = 12）或临床结局预测（n = 50），主要针对院外心脏骤停/OHCA（n = 62）、心血管疾病/CVDs（n = 19）和创伤（n = 24）。这些ML算法的表现各不相同，受试者操作特征曲线（AUC）下的中位数面积为85.6%，准确率为88.1%，灵敏度为86.05%，特异性为86.5%。在运营研究中，大多数ML算法的运营任务是救护车分配（n = 21），其次是救护车检测（n = 5）、救护车部署（n = 5）、路线优化（n = 5）和质量保证（n = 3）。所有运营ML算法的表现各不相同，AUC中位数为96.1%，准确率为90.0%，灵敏度为94.4%，特异性为87.7%。一般来说，神经网络和集成算法在一定程度上优于其他ML算法。
ML算法可改善不同院前医疗状况的分诊和管理以及提高救护车表现。未来的报告应聚焦于特定的临床状况或运营任务，以提高ML模型表现指标的精度。
