Design for a Digital Twin in Clinical Patient Care
This work addresses the problem of translating Digital Twins into clinical workflows for healthcare professionals, though it appears incremental as it builds on existing concepts without specifying novel breakthroughs.
The authors tackled the challenge of personalizing clinical patient care by proposing a general Digital Twin design that integrates knowledge graphs and ensemble learning to model a patient's clinical journey and support clinician decision-making, resulting in a predictive, modular, and interpretable system.
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.