Design for a Digital Twin in Clinical Patient Care
Design for a Digital Twin in Clinical Patient Care
Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements dictated by established clinical workflows. We present a generalizable 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 Digital Twins can be predictive, modular, evolving, informed, interpretable and explainable with applications ranging from oncology to epidemiology.
Anna-Katharina Nitschke、Carlos Brandl、Fabian Egersd?rfer、Magdalena G?rtz、Markus Hohenfellner、Matthias Weidemüller
Physikalisches Institut, Universit?t Heidelberg, Heidelberg, GermanyPhysikalisches Institut, Universit?t Heidelberg, Heidelberg, GermanyPhysikalisches Institut, Universit?t Heidelberg, Heidelberg, GermanyJunior Clinical Cooperation Unit 'Multiparametric Methods for Early Detection of Prostate Cancer', German Cancer Research CenterDepartment of Urology, Heidelberg University Hospital, Heidelberg, GermanyPhysikalisches Institut, Universit?t Heidelberg, Heidelberg, Germany
临床医学医学研究方法肿瘤学
Anna-Katharina Nitschke,Carlos Brandl,Fabian Egersd?rfer,Magdalena G?rtz,Markus Hohenfellner,Matthias Weidemüller.Design for a Digital Twin in Clinical Patient Care[EB/OL].(2025-05-02)[2025-07-23].https://arxiv.org/abs/2505.01206.点此复制
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