个性化计算模型用于构建医疗数字双胞胎
Adam Knapp1, Daniel A Cruz1, Borna Mehrad1
1Department of Medicine, University of Florida, Gainesville, FL, USA.
Journal of the Royal Society, Interface
|July 1, 2025
概括
数字双胞胎技术正在适应医疗保健,面临着个性化复杂计算模型的挑战. 我们介绍了一种新的算法,使用集成卡尔曼波器来弥合宏观状态和微观状态数据,以改善患者特定预测.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 数字健康数字健康
背景情况:
- 数字双胞胎技术起源于工程,越来越多地应用于生物医学.
- 用患者数据来个性化计算模型对于推进个性化医学的发展至关重要.
- 复杂的生物医学模型,包括基于代理的模型,缺乏标准化的个性化方法.
研究的目的:
- 开发一种新的算法,以动态校准复杂的生物医学模型,以满足个体患者的需求.
- 为了弥合临床可测量的宏观状态和详细的微观状态数据之间的差距,以实现模型个性化.
- 在个性化医学中提高基于模型的预测的准确性.
主要方法:
- 在宏观状态层面应用集体卡尔曼波器,一种数据同化技术.
- 将卡尔曼过器中的宏观状态更新与相应的微观状态更新联系起来.
- 确保微态与所需的宏态和模型动态保持一致.
主要成果:
- 已经提出了个性化复杂生物医学模型的新算法.
- 该方法有效地弥合了宏观状态和微观状态数据,以改进模型校准.
- 基于代理的模型和其他复杂的生物医学模拟的增强个性化.
结论:
- 拟议的算法提供了一个标准化的方法来个性化复杂的生物医学模型.
- 这种方法提高了患者特定预测的准确性,推进了个性化医疗.
- 促进数字双胞胎技术在临床实践中的整合.
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