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作为精准医学工具的健康数字孪生:计算、实施和监管方面的考量

Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.

作者信息

Venkatesh Kaushik P, Raza Marium M, Kvedar Joseph C

机构信息

Harvard Medical School, Boston, MA, USA.

出版信息

NPJ Digit Med. 2022 Sep 22;5(1):150. doi: 10.1038/s41746-022-00694-7.

Abstract

Health digital twins are defined as virtual representations ("digital twin") of patients ("physical twin") that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables. With appropriate use, HDTs can model random perturbations on the digital twin to gain insight into the expected behavior of the physical twin-offering groundbreaking applications in precision medicine, clinical trials, and public health. Main considerations for translating HDT research into clinical practice include computational requirements, clinical implementation, as well as data governance, and product oversight.

摘要

健康数字孪生被定义为患者(“物理孪生”)的虚拟表示(“数字孪生”),它由多模态患者数据、人群数据以及患者和环境变量的实时更新生成。通过适当使用,健康数字孪生可以对数字孪生上的随机扰动进行建模,以深入了解物理孪生的预期行为,从而在精准医学、临床试验和公共卫生领域提供开创性的应用。将健康数字孪生研究转化为临床实践的主要考虑因素包括计算要求、临床实施以及数据治理和产品监督。

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