Artificial Intelligence–Driven Norepinephrine Control Using Adaptive Cardiac Digital Twin for Critical Care Patients

Abstract Infusion pumps are used in critical care settings to deliver medicines such as norepinephrine (NE), insulin, and certain nutrients. Currently, clinicians preset the infusion rate in the device, usually for 1 hour, resulting in a constant drug delivery. However, a patient's response to a preset infusion can vary over time depending on their vital changes. In the case of NE, dosage errors are reported due to delayed infusion and interpatient variability in hemodynamics. To automate the infusion process, fuzzy logic, and proportional integral derivative controllers have been used, but they have fixed algorithms and lack clinical validation. This study proposes a data-driven artificial intelligence (AI)–digital twin adaptive framework for NE infusion to reduce the risks of hypotension and hypertension. For personalized infusion, data were collected from patients supported with NE. A random forest decision model (RFDM) was used to predict the NE rates based on varying hemodynamics. The model performance at each time step is evaluated using a long short-term memory network that works as an adaptive digital twin cardiac model (DTCM). Virtual automation of the infusion process is performed using outputs of AI models. The RFDM achieved strong predictive accuracy, with a mean absolute percentage error (MAPE) of 5.3% and an R2 score of 0.97. MAPE values of DTCM for systolic, diastolic, and mean arterial blood pressure (BP) were 5.56, 5.79, and 5.36%, respectively. Compared with real-world infusion, the virtual automation reduced drug consumption by 13.82%. The adaptive DTCM resembles real-world hemodynamics and has improved performance across various infusion rates. RFDM combined with DTCM provides an alternative method for NE infusion. The simulated evaluation shows that the proposed framework maintains BP, automates the infusion process, enabling patient-specific personalized dosing.