DataSheet1_AI algorithm for personalized resource allocation and treatment of hemorrhage casualties.docx

A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs versus a static population-based method that only uses currently available vital-sign information. The theoretical benefits of this approach included up to 46% additional casualties restored to healthy vital signs and a 119% increase in fluid-utilization efficiency. Although the study is not immune from limitations associated with synthetic data under specific assumptions, the work demonstrated the potential for incorporating neural network-based AI technologies in hemorrhage detection and treatment. The simulated injury and treatment scenarios used delineated possible benefits and opportunities available for using AI in pre-hospital trauma care. The greatest benefit of this technology lies in its ability to provide personalized interventions that optimize clinical outcomes under resource-limited conditions, such as in civilian or military mass-casualty events, involving moderate and severe hemorrhage.

本研究针对基于深度神经网络（Deep Neural Network，DNN）的人工智能（AI）模型展开评估，旨在考察其在预测出血患者生命体征、优化批量伤亡患者液体复苏管理方面的应用价值。研究借助心肺计算模型生成出血性伤亡患者的合成数据，开发了一款应用程序：仅需利用患者最初10分钟的生命体征监测数据流，即可预测60分钟后不同液体复苏分配方案的实施效果。随后将预测得到的结果用于为各类模拟批量伤亡场景选取最优的复苏方案。借此可对比评估两种复苏分配策略的潜在优势：一种是基于未来生命体征个性化预测的分配方法，另一种是仅使用当前可用生命体征信息的静态人群基准分配方法。该策略的理论收益包括：可多挽救46%的伤亡患者，使其恢复至健康生命体征水平，同时将液体使用效率提升119%。尽管本研究无法规避特定假设下合成数据所带来的局限性，但该研究证实了将基于神经网络的AI技术应用于出血检测与治疗的潜力。本次研究所采用的模拟创伤与治疗场景，明确了AI在院前创伤救治中可发挥的潜在价值与应用空间。该技术最大的优势在于，能够在资源受限的场景下（如民用或军事批量伤亡事件中涉及中重度出血的情况）提供个性化干预措施，从而优化临床结局。