| Abstract: |
| This presentation introduces an extension to system theory as a novel framework for modeling clinical data under significant uncertainty and poor identifiability conditions, common challenges in medical systems. These challenges arise from ethical, safety, and regulatory constraints, limiting the persistent drug-related excitation of the human body. Moreover, the drug-dose effect relationship is complicated by substantial inter- and intra-patient variability. The absence of suitable instrumentation for direct measurement, relying instead on inferences and surrogate metrics, adds further complexity. The efficacy of our approach was examined in clinical data from patients monitored during the induction phase of target-controlled intravenous anesthesia. The proposed method delivered models with physiological explainable parameters and suitable for closed-loop control of anesthesia. A notable advantage of this approach is its robustness in the face of uncertainty. The work is the first piece of the puzzle towards Extended Reality solutions encompassing virtual, augmented, and mixed reality in general anesthesia management. |
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