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Blood transfusion is a critical medical procedure that saves lives and treats various medical conditions. It involves transferring blood from a donor to a recipient. This process requires a thorough understanding of the ABO blood group system and its associated antigens and antibodies.
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A blood transfusion is a medical procedure used to replace blood lost due to injury, surgery, or to treat conditions such as anemia or cancer. During a transfusion, donor blood is...
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Blood transfusion is a therapeutic measure to restore the blood volume after extensive blood loss due to an accident or a medical procedure. Blood transfusion involves drawing a certain amount of blood from a suitable donor and infusing it into the recipient.
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The history of blood transfusion dates back to the 17th century, when early attempts were made in animals. In 1818 James Blundell, a British doctor, performed the first successful human blood transfusion. Later in 1900, Karl...
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机器学习模型用于预测在多重创伤患者中预先进行红细胞输血的需要.

Saeed Safari1,2,3, Hamed Zarei3,4, Kiarash Zare3

  • 1Research Center for Trauma in Police Operations, Directorate of Health, Rescue & Treatment, Police Headquarter, Tehran, Iran.

Archives of academic emergency medicine
|February 10, 2026
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概括

机器学习模型准确地预测了创伤患者需要输血红细胞 (PRBC) 的需要. 关键预测指标包括格拉斯哥昏迷表,血红蛋白,脉率,静脉血压和脉压.

关键词:
格拉斯哥昏迷尺度 格拉斯哥昏迷尺度机器学习 机器学习数学模型是一个数学模型.伤口和伤害的伤口和伤害.

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科学领域:

  • 医疗信息学 医疗信息学
  • 创伤外科 手术 创伤外科
  • 紧急医疗 紧急医疗

背景情况:

  • 出血性休克是可预防的创伤死亡的主要原因.
  • 早期识别和干预对于管理出血性休克至关重要.
  • 预测工具可以帮助创伤患者及时做出输血决定.

研究的目的:

  • 开发和优化机器学习 (ML) 算法,以预测在多重创伤患者受伤后24小时内需要输血红细胞 (PRBC).
  • 确定影响早期PRBC输血要求的关键临床预测因素.

主要方法:

  • 对908名多重创伤患者的回顾性分析.
  • 利用SHAP分析进行特征选择,确定格拉斯哥昏迷尺度 (GCS),血红蛋白 (Hb),脉冲率 (PR),静脉血压 (SBP) 和脉冲压作为关键预测因素.
  • 使用AUC,F1得分,灵敏度和特异性评估了多个ML算法 (随机森林,K-最近邻居,后勤回归).

主要成果:

  • 随机森林模型表现出卓越的性能,AUC为0.997,灵敏度为0.938,特异性为0.994.
  • PRBC输血与较低的GCS,较高的PR,较低的SBP,较低的脉压和较低的Hb水平有关.
  • K-近邻和物流回归也显示出高特异性,但敏感性较低.

结论:

  • 机器学习,特别是随机森林算法,有效地预测了创伤患者早期PRBC输血的需要.
  • GCS,Hb,PR,SBP和脉压是早期输血的重要预测因素.
  • 建议进行进一步的多中心验证,以确认这些预测模型的临床适用性.