2025/05/02 by Nitschke, Anna-Katharina, Brandl, Carlos, Egersdörfer, Fabian +3
#Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Medical Physics (physics.med-ph) #Software Engineering (cs.SE) #and Science (cs.CE)
paper · doi:10.48550/arxiv.2505.01206
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.