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通过机器学习和人工智能推进肺移植.

Lielle Ronen1,2,3, Shaf Keshavjee1,2,3,4, Andrew T Sage1,2,3,4,5

  • 1Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network.

Current opinion in pulmonary medicine
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概括
此摘要是机器生成的。

人工智能 (AI) 和机器学习 (ML) 正在肺移植中出现,用于预测结果和优化药物剂量. 未来的应用有望提高患者的生存率和供体肺部利用率.

关键词:
人工智能的人工智能是人工智能.活体肺外 perfusion 透 肺外 perfusion 活体肺外 perfusion 透肺部移植 肺部移植机器学习是机器学习.这是一个多式联络模式.结果预测结果预测.时间序列预测时间序列预测

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学
  • 移植科学 移植科学

背景情况:

  • 肺移植是一种复杂的手术,在患者管理和结果预测方面存在重大挑战.
  • 人工智能 (AI) 和机器学习 (ML) 的采用为应对这些挑战提供了新的方法.

研究的目的:

  • 审查AI和ML在肺移植中的当前应用.
  • 探索AI/ML在预测结果和药物剂量方面的作用.
  • 讨论这些技术在现场的未来潜在用途和风险.

主要方法:

  • 审查现有的关于AI和ML在肺移植中的应用文献.
  • 分析用于预测短期和长期移植结果而开发的模型.
  • 检查AI/ML方法来优化药物剂量,例如Tacrolimus.

主要成果:

  • 已经开发出AI/ML模型来预测初级移植功能障碍,输出时间,患者存活率和慢性肺异位移植功能障碍.
  • 概念验证模型展示了AI/ML在时间序列药物剂量的实用性,以Tacrolimus为例.
  • 早期整合ML模型显示了改善临床决策的前景.

结论:

  • 在肺移植中,AI和ML的整合可以提高移植后的生存率,并优化捐赠者的肺部利用率.
  • 数据采集的进步,包括实时监控,将推动更复杂的ML模型.
  • 该领域准备迎来重大演变,AI/ML在肺移植护理中发挥着越来越重要的作用.