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现场预测 - 预测对创伤患者的大规模输血,ICU利用和机械通风的需求

Allan E Stolarski^1,2, Kevin J Brady³, Jonathan D Stallings⁴

¹Division of Traumatology, Surgical Critical Care, University of Pennsylvania, Philadelphia, PA 19104, United States.

Military medicine

|March 5, 2026

概括

现场人工智能选 (FAIT) 工具通过整合外部损伤数据与生命体征来改善创伤患者的评估. 这种人工智能驱动的方法提高了对平民和军事伤亡的关键护理需求的预测.

科学领域:

创伤护理和紧急医疗
医疗保健中的人工智能
临床决策支持系统临床决策支持系统

背景情况:

准确和快速的患者分组对于创伤护理中有效的资源配置至关重要.
现有的分类算法通常仅依赖生命体征,可能缺少外部损伤的关键信息.
现场人工智能选 (FAIT) 工具是为了通过纳入伤害细节来提高选准确性而开发的.

研究的目的:

开发和验证人工智能工具 (FAIT),用于预测创伤患者的资源需求.
评估是否结合外部伤害信息可以提高分拣性能,而不是仅仅与生命体征相比.
评估FAIT在平民和军事创伤群体中的有效性.

主要方法:

利用大规模的民用 (TQIP) 和军事 (DoDTR) 创伤数据集.
开发了一个物流回归模型,其中包含了生命体征和损伤特征 (机制,位置).
使用10倍交叉验证验证模型,以ROC AUC作为主要性能指标.

主要成果:

对于民用和军事患者来说,FAIT在多种结果 (大规模输血,机械通风,ICU入院) 中显著提高了预测准确性.
对于平民患者,ROC AUC从0.70提高到0.89大规模输血和0.65到0.90机械通风.
对于军事患者,ROC AUC从0.67提高到0.88大规模输血和0.76到0.90机械通风.

结论:

FAIT工具有效地支持在伤害发生时进行早期评估.
FAIT在预测民用和军事环境中对各种创伤机制的资源配置方面表现出显著的能力.
将外部损伤数据与生命体征集成,可以提高人工智能驱动的分拣性能.

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