The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states.
While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.
Patients scheduled for elective surgery requiring general anesthesia.
Procedures requiring continuous depth of anesthesia monitoring (BIS).
None
Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.
Cohort
Other
Not listed
Calibration error of the predictive uncertainty cone
Calibration error of the predictive uncertainty cone - Calibration error of the predictive uncertainty cone is the discrepancy between a model's stated confidence level (e.g., predicting that 95% of future values will fall within a specific range) and the actual frequency with which the true values actually land inside that predicted boundary.
Mean Absolute Error (MAE)
Mean Absolute Error (MAE)
Trend accuracy
Trend accuracy measures a predictive model's ability to correctly forecast the future direction and rate of change of a variable (such as whether a patient's anesthesia depth is actively lightening or deepening), independent of the absolute numerical error at any single point in time.
Root Mean Square Error (RMSE)
Root Mean Square Error (RMSE)
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Universitair Ziekenhuis Brussel
Lead sponsor
AZ Sint-Jan AV
Collaborator
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