Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements emerging from established clinical workflows. We present a general and unspecialized Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such a design is predictive, modular, evolving, informed, interpretable and explainable, thus opening broad clinical applications.

翻译：数字孪生在实现个性化临床患者照护方面具有巨大潜力，前提是该概念需根据现有临床流程产生的具体需求进行转化。我们提出一种通用且非专业化的数字孪生设计，结合知识图谱与集成学习，以反映患者的完整临床历程并辅助临床医生决策。该设计具备预测性、模块化、演进性、信息驱动、可解释性与可说明性，从而拓展了广泛的临床应用场景。