A digital twin model for evidence-based clinical decision support in multiple myeloma treatment - Grieb, Nora; Schmierer, Lukas; Kim, Hyeon Ung; Strobel, Sarah; Schulz, Christian; Meschke, Tim; Kubasch, Anne Sophie; Brioli, Annamaria; Platzbecker, Uwe; Neumuth, Thomas; Merz, Maximilian; Oeser, Alexander (Frontiers in Digital Health, 2023)
Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decisionmaking tools are to be included into clinical routine.