NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction

2025-03-20

AI Summary

To address the clinical need for early sepsis warning in intensive care units (ICUs), this paper proposes a novel framework integrating neural networks, contextual multi-armed bandits, and conformal prediction. The method explicitly models patient-specific reward functions based on dynamic clinical time-series features and quantifies prediction uncertainty under offline data distributions, yielding confidence intervals with statistical guarantees (e.g., 90% coverage). Its key innovation lies in the first integration of conformal prediction into a contextual bandit decision-making framework, enabling real-time, uncertainty-aware intervention recommendations. Evaluated on a real-world ICU dataset, the model achieves an AUC of 0.92 for sepsis prediction, with an average early warning lead time of 4.3 hours; crucially, its empirical coverage strictly satisfies the prespecified confidence level. This significantly enhances both reliability and interpretability of clinical decision support.

Technology Category

• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Early sepsis prediction using deep learning and contextual bandits
• Modeling patient-specific reward functions under uncertainty
• Integrating neural networks with conformal prediction for robust decisions

Innovation

• Deep learning integrates contextual multi-armed bandits
• Neural networks model personalized reward functions
• Conformal prediction quantifies uncertainty in sepsis detection