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使用基于模拟的推理构建虚拟患者.

Nathalie Paul1, Venetia Karamitsou2, Clemens Giegerich2

  • 1Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany.

Frontiers in systems biology
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

基于模拟的推断 (SBI) 通过从临床数据中学习概率分布来生成虚拟患者进行in silico试验. 这种方法捕捉了患者的变性,并通过提供可能的替代虚拟患者群体来增强药物开发.

关键词:
质量服务提供商 (QSP) 建模.人工智能的人工智能是人工智能.个体患者适合的装备.机器学习是机器学习.基于模拟的推理推理.虚拟患者是虚拟患者.

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科学领域:

  • 计算生物学和药理学 计算生物学和药理学
  • 医疗保健中的机器学习

背景情况:

  • 定量系统药理 (QSP) 模型模拟病理生理学和药理学,用于药物开发和治疗反应预测.
  • 从高维度,稀疏和杂的临床数据中为QSP模型生成"虚拟患者"是一个重大挑战.
  • 临床变异性需要强大的方法来创建多样化和代表性的虚拟患者群体.

研究的目的:

  • 调查基于模拟的推理 (SBI) 在虚拟患者生成中使用个体患者数据的适用性.
  • 开发和评估新的"最接近患者适合" (SBI NPF) 概念,以提高适合性能.
  • 评估SBI在捕捉诸如类风湿性关节炎等复杂疾病中的患者间变异性的能力.

主要方法:

  • 应用基于模拟的推断 (SBI),一种概率的机器学习技术,用于从临床数据中生成虚拟患者.
  • 开发并验证了最接近患者匹配 (SBI NPF) 方法,以改善参数化.
  • 使用类风湿性关节炎作为案例研究,因为难以预测治疗反应.

主要成果:

  • SBI方法有效地捕获了类风湿性关节炎临床数据中的实质性患者间变异性.
  • 与已有的装配技术相比,SBI方法显示出具有竞争力的性能.
  • SBI自然为参数化提供概率分布,使其能够生成可能的替代虚拟患者群体.

结论:

  • 基于模拟的推断是一种可行且强大的方法,可以从个别临床数据中生成虚拟患者.
  • 开发的SBI NPF方法提高了虚拟患者创建的准确性和稳定性.
  • 通过SBI生成的虚拟患者群体可能有助于提高在 silico 临床试验中对候选药物的评估.