迈向机理医学数字双胞胎:免疫学中的一些用例。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2024-03-07 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1349595
Reinhard Laubenbacher, Fred Adler, Gary An, Filippo Castiglione, Stephen Eubank, Luis L Fonseca, James Glazier, Tomas Helikar, Marti Jett-Tilton, Denise Kirschner, Paul Macklin, Borna Mehrad, Beth Moore, Virginia Pasour, Ilya Shmulevich, Amber Smith, Isabel Voigt, Thomas E Yankeelov, Tjalf Ziemssen
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引用次数: 0

摘要

个性化医疗面临的一个基本挑战是,如何充分捕捉个体患者的复杂性,以确定保持其健康或恢复其健康的最佳方法。这就要求个性化计算模型具有足够的分辨率和足够的机理信息,以便为临床医生提供可操作的信息。这种个性化模型越来越多地被称为医学数字孪生。用于医疗应用的数字孪生技术仍处于起步阶段,需要进行广泛的研究和开发。本文重点介绍几个处于不同开发阶段的项目,这些项目可以开发出具体实用的医疗数字孪生或数字孪生建模平台。本文是为期两天的医学数字孪生相关问题论坛的成果,尤其是那些涉及免疫系统的问题。论坛讨论的视频录像可公开获取。
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Toward mechanistic medical digital twins: some use cases in immunology.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

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来源期刊
CiteScore
4.20
自引率
0.00%
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审稿时长
13 weeks
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