ACUMEN: Active Cross-Entropy Method with Uncertainty-driven Neural ODEs for Data-Efficient System Identification in Healthcare

Building personalized, data-driven models of patient response to therapies with large parameter spaces, such as Deep Brain Stimulation, is a major challenge in precision medicine. We present ACUMEN, a data-efficient framework for digital twin–inspired modeling that couples Neural Ordinary Differential Equations (Neural ODEs) with uncertainty-driven planning via Cross-Entropy Method Model Predictive Control (CEM-MPC). An ensemble of Neural ODEs captures physiological dynamics, with ensemble disagreement quantifying epistemic uncertainty. CEM-MPC selects exploratory interventions under smoothness constraints, aided by optimistic state progression and adaptive scaling across state dimensions. In the Reinforcement Learning for Deep Brain Stimulation (RL-DBS) synthetic environment, ACUMEN reduces test error by up to 24.2\% over passive data collection, while producing tighter uncertainty estimates. These results highlight ACUMEN’s potential to lower sample complexity and enable safer, more personalized system identification in data-limited healthcare-inspired settings.