Data-Driven Anesthesia: An Ensemble Model for Propofol and Remifentanil Dosage Control During Medical Surgery

Anesthesia is a critical medical intervention used to ensure patients remain unconscious, pain-free, or immobile during surgical and diagnostic procedures. The choice of anesthetics is influenced by the type of surgery, the patient’s medical history, and the preferences and expertise of the anesthesiologist. Anesthetics is usually administered through inhalation, intravenous injection, or a combination of both. Administering anesthesia during medical procedures is vital to patient care, requiring precision, flexibility, and real-time adaptability. In this work, we propose a new machine learning model that relies on LSTM and a fully connected neural network to control the patient’s anesthetic state during surgery for all stages including induction, maintenance, and emergence, using a synergy of Propofol and Remifentanil. Propofol is used primarily for sedation, whereas Remifentanil is mainly used for pain relief. Since the duration of their effect is very short, constant infusion of both drugs is necessary to maintain the patient’s sedation state. Experience indicates that the synergistic effect of both drugs yields better control of the anesthesia level. This model is meant to elevate the burden that comes with the task of anesthesia control in real-time but shouldn’t take complete control as the presence of anesthesiologists remains vital to monitor its performance.