开发一种基于人工智能的虚拟患者模型,用于实时诊断急性呼吸困扰综合征
Chadi S Barakat1,2,3, Konstantin Sharafutdinov3,4, Josefine Busch1
1Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
研究人员开发了一种深度学习模型,以预测虚拟患者的急性呼吸困扰综合征 (ARDS) 发病情况. 这种人工智能工具准确地模拟了疾病的进展,有助于更快地诊断和治疗重症监护病人的治疗支持.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 计算生理学计算生理学
背景情况:
- 急性呼吸困扰综合征 (ARDS) 对重症监护室 (ICU) 患者构成重大威胁,其特点是肺功能障碍逐渐恶化.
- 尽管正在进行的研究和诊断标准如柏林定义,但ARDS的异质性使诊断和治疗复杂化.
- 现有的ARDS进展建模工具存在,但需要有效的计算方法来临床应用.
研究的目的:
- 在虚拟患者模拟器中开发基于深度学习的替代模型,用于预测急性呼吸困扰综合征 (ARDS) 发作.
- 利用机器学习和高性能计算来快速开发专门的诊断支持系统.
- 通过现有机械模拟器,展示创建精确,轻量级AI模型的概念验证.
主要方法:
- 使用高性能计算数据科学平台进行模型开发.
- 应用先进的机器学习和数据分析技术,包括高效的超参数调整.
- 开发了一个基于诺丁汉生理模拟器的深度学习代孕模型,用于ARDS发病预测.
主要成果:
- 开发的轻量级深度学习模型实现了高精度,与原始模拟器相比 (每个参数R2>0.90).
- 该过程证明了快速创建复杂生理模拟的准确代孕模型的可行性.
- 该研究验证了在医疗保健中使用超级计算基础设施和开源软件用于AI模型开发的有效性.
结论:
- 深度学习提供了一种可行的方法,可以为诺丁汉生理学模拟器等生理学模拟器创建准确的替代模型.
- 开发的模型可以作为急性呼吸困扰综合征的专门诊断支持系统.
- 这种方法促进了人工智能驱动的临床工具的快速开发和传播,并得到了超级计算和开源解决方案的支持.
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