使用机器学习计算来预测边界跌倒患者四肢骨折的因素

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Carlos Palacio¹, Maximillian Hovorka¹, Marie Acosta¹

¹South Texas Health System - McAllen Department of Trauma, McAllen, TX, 78503, USA.

|July 4, 2024

概括

美国 - 墨西哥边境的伤害与特定的落机制有关. 了解这些因素,比如从边界墙上落或车辆喷射,对于预测和治疗解剖损伤至关重要.

科学领域:

创伤外科手术创伤外科
公共卫生公共卫生
生物力学生物力学

背景情况:

美国 - 墨西哥边境的布是由各种因素造成的,包括边境墙/围的塌,逃离边境巡逻以及车辆的喷射.
这些事件导致显著的解剖损伤,包括四肢骨折和内部器官损伤.
确定具体的原因及其相关的损伤模式对于临床管理至关重要.

研究的目的:

确定导致在美墨边境发生解剖损伤的因素.
识别和排名导致四肢骨折和内部器官损伤的因素的重要性.
开发基于多因素评估的临床应用的预测模型.

主要方法:

这是一项对178名因肌肉骨或内部器官受伤而入院的患者的回顾性研究.
对人口统计学,并发症和下降机械因素的分析.
利用多层感知神经网络 (MPNN) 进行预测因素识别和重要性分层.

主要成果:

肢体骨折与边界墙/围倒塌 (p=0.001) 和逃跑 (p=0.002) 有显著的相关性.
脊柱骨折与跳过桥梁/围相关 (p=0.007,p=0.026).
车辆喷射/MVA与头部,胸部和腹部受伤有很强的相关性 (P<0.001).

结论:

不同的受伤因素导致不同的解剖损伤.
多因素评估提高了与边界有关的伤害的诊断准确性.
这些发现支持开发用于临床使用的预测模型.

关键词:

边界下降是一个边界下降.截肢骨折截肢骨折截肢骨折截肢骨折截肢骨折 MPNN MPNN 在线观看机器学习是机器学习.

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