Blending Physics and Data to Model Hemodynamic Effects Under General Anesthesia

General anesthesia, typically induced using a combination of hypnotic (propofol) and analgesic (remifentanil) drugs, is crucial for the success of surgical procedures, but it can cause dangerous cardiovascular side effects. In this context, models and simulations offer new opportunities to address the intrinsic complexity of the process, accelerating advances and innovation in the technology of anesthesia. This study aims to improve the modeling of hemodynamic effects under general anesthesia by expanding the applicability of a recent mechanistic model in combination with data-driven modules. In particular, we use a dataset related to plastic surgery for both model calibration and testing, preserving the physical interpretability of the mechanistic model while integrating it with data-driven components to enhance its predictive capabilities. The results demonstrate a significant improvement in the model ability to simulate hemodynamic variables under surgical conditions, offering potential applications for anesthesia monitoring and control systems design that consider the patient's cardiovascular safety. This enhanced hybrid model provides a more accurate representation of the complex interactions between anesthetic drugs and cardiovascular dynamics in real surgical settings.