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使用人工智能 (GEMA-AI) 进行肝脏分配的性别平等模型,用于等候名单肝脏移植优先级.

Antonio Manuel Gómez-Orellana1, Manuel Luis Rodríguez-Perálvarez2, David Guijo-Rubio3

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一个新的人工智能评分,GEMA-AI,准确地预测肝移植等待名单的结果,特别是对于更生病的患者和女性. 这种先进的模型比现有方法提供了更好的优先级,可能挽救生命.

关键词:
人工神经网络的人工神经网络不平等的差异 在可解释的人工智能性别 性别 性别 性别肝脏分配 分配 肝脏分配机器学习 机器学习

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科学领域:

  • 肝病学 肝病学是一种肝病学.
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 肝移植 (LT) 等候名单优先级模型对于公平的器官分配至关重要.
  • 现有的模型可能无法完全捕捉患者结果的复杂性.
  • 需要提高预测准确性来识别处于最高风险的患者.

研究的目的:

  • 开发和验证人工智能 (AI) 评分,GEMA-AI,用于预测LT等候名单的结果.
  • 将GEMA-AI的性能与使用相同输入变量的既定模型进行比较.
  • 评估非线性AI方法对患者优先级的影响.

主要方法:

  • 在英国 (2010-2020年) 培训/内部验证和澳大利亚 (1998-2020年) 外部验证的成年LT候选人的队列研究.
  • GEMA-AI使用一种可解释的人工神经网络,包括国际规范比率,胆红素,和淋巴膜过率.
  • 与GEMA-Na,MELD 3.0和MELD-Na进行等候名单优先排序的比较.

主要成果:

  • 与GEMA-Na,MELD-Na和MELD 3.0相比,GEMA-AI在内部和外部验证队列中都表现出更好的歧视.
  • 人工智能模型在女性和具有极端分析值的患者中显示出更明显的好处.
  • 过渡到GEMA-AI可以重新优先考虑6.4%的患者,并可能挽救整体59例死亡中的1例,其中包括13例女性死亡中的1例.

结论:

  • 像GEMA-AI这样的可解释的机器学习模型可能会在LT等待列表优先级方面表现优于传统的回归模型.
  • GEMA-AI提供了更准确的等待列表结果预测,特别是在重症患者.
  • 该研究强调了人工智能的潜力,以提高器官分配系统的公平性和有效性.