A NEURAL NETWORK APPROACH TO PEDIATRIC TRAUMA TRIAGE

描述(由申请人提供)：伤害导致的儿童死亡人数超过所有其他原因的总和。由于受伤儿童在接受专门创伤护理的中心有更好的预后，适当的交通工具可能会降低发病率和死亡率。必须使用院前信息来确定是否需要将患者送往创伤中心，并规划到达后的护理。根据院前信息，对几种分类方案进行了评估，以对受轻伤和重伤的儿童进行分层。它们使用了不同范围的输入，包括生理参数、损伤的解剖位置和损伤机制，并使用了线性分析和Logistic回归技术。尽管做出了这些努力，但还没有一种儿科创伤分诊方法达到足够准确(避免分诊不足或过度)和可重复性的目标。使用标准回归和分类技术制定的分诊标准的有限成功的一个潜在解释是院前数据的复杂性以及这些数据与结果的不确定关系。当分类需要使用不同类型的输入数据，或者这些变量与最终分类之间的关系被模糊地理解时，神经网络是一类比传统方法更具优势的模型。虽然神经网络在分诊中的应用尚未被描述，但在预测成人创伤患者的发病率和死亡率方面，神经网络比Logistic回归方法更好。神经网络可能非常适合于儿科创伤分诊分析，因为院前变量的多样性和年龄特异性，以及这些变量与受伤儿童预后之间的不确定关系。这些观察结果导致了一种假设，即神经网络可以比目前的分诊方法更准确地根据创伤的严重程度和对医院资源的需求对儿科创伤患者进行分类。将训练神经网络，利用院前提供者可获得的信息，根据受伤的严重程度和对医院资源的需求对受伤儿童进行分类。将训练网络的分类精度与基于现有分类策略的分类精度进行比较。该项目的长期目标是开发和实施一个基于神经网络的受伤儿童分类工具。

DESCRIPTION (provided by applicant): Injuries result in more deaths in children than all other causes combined. Because injured children have better outcomes at centers with specialized trauma care, appropriate transport to these centers may reduce morbidity and mortality. Prehospital information must be used to determine the need for transport to a trauma center and for planning care after arrival. Several triage schemes have been evaluated for stratifying minimally and severely injured children based on prehospital information. These have used a diverse range of inputs, including physiological parameters, anatomic site of injury and mechanism of injury, and have used both linear techniques of analysis and logistic regression. Despite these efforts, no pediatric trauma triage method has yet met the goal of being sufficiently accurate (avoiding under- or over-triage) and reproducible. One potential explanation for the limited success of triage criteria developed using standard regression and classification techniques is the complexity of prehospital data and the uncertain relationship of this data to outcome. Neural networks are a family of models that have an advantage over conventional methods when classification requires using different types of input data or the relationships between these variables and the final classification are vaguely understood. While their use for triage has not been described, neural networks have been better than logistic regression methods in predicting morbidity and mortality in adult trauma patients. Neural networks may be well suited for analysis of pediatric trauma triage because of the diversity and age-specificity of prehospital variables and the uncertain relationship between these variables and the outcome of injured children. These observations have led to the hypothesis that a neural network can more accurately classify pediatric trauma patients by severity of injury and need for hospital resources than current triage methods. Neural networks will be trained to classify injured children based on severity of injury and need for hospital resources using information available to prehospital providers. The classification accuracy of trained networks will be compared with that based on current triage strategies. The long-term goal of this project is to develop and implement a tool for triaging injured children based on neural networks.