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机器学习工具在移植医学中的临床部署:未来会发生什么?

Madhumitha Rabindranath^1,2,3, Maryam Naghibzadeh¹, Xun Zhao¹

¹Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.

Transplantation

|July 23, 2024

概括

机器学习 (ML) 在移植医学中为患者优先级和结果预测提供了潜力. 部署这些ML工具需要跨学科的团队,可靠的数据,并解决临床使用的实施障碍.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
移植医学移植医学移植医学

背景情况:

机器学习 (ML) 在分析患者数据以进行临床决策和个性化结果方面表现有前途.
在移植中ML的应用包括移植前患者优先级,捐赠者-接受者匹配,器官分配和移植后结果预测.
尽管有发展,但很少有ML工具在临床环境中部署.

研究的目的:

要总结目前在器官移植中的ML应用.
讨论在移植环境中临床部署ML工具的框架.

主要方法:

审查现有的关于ML在移植中的应用文献.
确定临床部署框架的关键组件.

主要成果:

ML模型已经在移植医学的各个方面证明了其实用性.
成功部署的关键要素包括跨学科团队,精心策划的数据集和解决实施障碍.

结论:

ML具有显著的潜力来增强移植医学.
需要一个结构化的方法来弥合ML模型开发和移植临床实施之间的差距.

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