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Abstract Digital twin technology, pioneered for engineering applications, is being adapted to biomedicine and healthcare; however, several problems need to be solved in the process. One major problem is that of dynamically calibrating a computational model to an individual patient, using data collected from that patient over time. This kind of calibration is crucial for improving model-based forecasts and realizing personalized medicine.
Abstract A medical digital twin is a computational replica of some aspect of a patient's biology relevant to patient health. It consists of a computational model that is calibrated to the patient and is dynamically updated using a stream of patient data. The underlying computational model is often complex, multi-state, stochastic, and combines different modeling platforms at different scales. Standard methods for data assimilation do not directly apply to such models.
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