人工智能
数字健康
疾病
数据科学
慢性病
代谢性疾病
计算机科学
医学
重症监护医学
内科学
医疗保健
病理
经济
经济增长
作者
Clara Mosquera-Lopez,Peter G. Jacobs
标识
DOI:10.1016/j.tem.2024.04.019
摘要
Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.
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