Virtually monitoring blood pressure levels in a patient using machine learning and digital twin technology

Abstract

A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's blood pressure. The platform implements a short-term prediction model to generate a daily prediction of the patient's blood pressure based on nutrition data reported by the patient and sensor data and lab test data collected for the patient. The platform implements a long-term prediction model generate a prediction of the patient's blood pressure during an extended time period based on sensor data and lab test data collected for the patient. Using the short-term prediction model, the long-term prediction model, or both, the patient health management platform generates predictions of the patient's diastolic and systolic blood pressure and updates a digital twin of the patient's metabolic profile.