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.
@article{arxiv.2505.01206,
title = {Design for a Digital Twin in Clinical Patient Care},
author = {Anna-Katharina Nitschke and Carlos Brandl and Fabian Egersdörfer and Magdalena Görtz and Markus Hohenfellner and Matthias Weidemüller},
journal= {arXiv preprint arXiv:2505.01206},
year = {2026}
}
Comments
Accepted version of the journal. Added clinical examples in the supplementary material and revised the introduction and section 3