Figure 3.

Flowchart illustrating the process for training a machine-learned model to output a representation of a patient’s metabolic health. This figure outlines the process used by the digital twin module to train a machine-learning model for predicting a patient’s metabolic state. The process begins with retrieving historical biological and patient data (710) to train a baseline model (720) reflecting population-level trends. Next, a patient-specific dataset (730) is generated, and a personalized model (740) is trained to predict individual metabolic responses. Finally, current biological and patient data are inputted into the trained model (750) to output real-time metabolic states. Data sources include lab tests, sensor data, and patient-recorded measurements, enabling precise metabolic monitoring and health predictions. This figure is taken from the patent US 2021/0196195 A1.