Personalized Blood Pressure Control by Machine Learning for Remote Patient Monitoring

Abstract

In the midst of a global health crisis, it is of utmost importance for healthcare technologies to possess the capability to regulate and monitor the physiological variables of patients remotely and automatically. The effective control of mean arterial pressure (MAP) in a closed-loop manner is particularly critical for individuals who are critically ill or in the process of recovering from surgical procedures. Within the framework of the present research, an adaptive closed-loop structure has been formulated with the objective of controlling a patient’s MAP through governed administration of the medication sodium nitroprusside (SNP), to attain the desired MAP levels under varying conditions. The proposed closed-loop technique incorporates an intelligent controller known as the active disturbance rejection control (ADRC) with the intention of tracking the desired MAP value, alongside the utilization of continuous action policy gradient (CAPG) for the optimization of the controller’s coefficients. Under the DRL strategy, an actor is responsible for generating policy requests, while a critic assesses the efficacy of the actor’s policy directives. This approach uses gradient descent to train the weight values of both actor and critic networks, and it is dependent on the reward return linked to the MAP fault. Upon comparing the outcomes of the recommended structure with conventional models, numerical simulation results demonstrate the superiority of the proposed system in coping with varying working conditions, key-value fluctuations, and uncertainties, while effectively maintaining the desired mean arterial pressure and drug administration rate.

Keywords

• Drug infusion pump
• mean arterial pressure (MAP)
• adaptive closed-loop strategy
• active disturbance rejection control (ADRC)
• deep reinforcement learning (DRL)