NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction
This work addresses the need for timely and accurate predictions to improve patient outcomes in critical care settings, representing an incremental advancement by combining existing techniques for personalized decision-making.
The paper tackles the problem of early sepsis prediction in critical care by modeling patient-specific reward functions in a contextual multi-armed bandit setting, resulting in a framework that provides actionable confidence bounds on predictions through the integration of neural networks and conformal prediction.
In critical care settings, timely and accurate predictions can significantly impact patient outcomes, especially for conditions like sepsis, where early intervention is crucial. We aim to model patient-specific reward functions in a contextual multi-armed bandit setting. The goal is to leverage patient-specific clinical features to optimize decision-making under uncertainty. This paper proposes NeuroSep-CP-LCB, a novel integration of neural networks with contextual bandits and conformal prediction tailored for early sepsis detection. Unlike the algorithm pool selection problem in the previous paper, where the primary focus was identifying the most suitable pre-trained model for prediction tasks, this work directly models the reward function using a neural network, allowing for personalized and adaptive decision-making. Combining the representational power of neural networks with the robustness of conformal prediction intervals, this framework explicitly accounts for uncertainty in offline data distributions and provides actionable confidence bounds on predictions.