用神经网络+置信区间，实现重症患者脓毒症的个性化早期预警。
NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction
- 用神经网络直接建模患者特异性奖励函数，实现动态决策。
- 结合置信区间，给出预测结果的可信度范围，降低误判风险。
- 适合关注临床决策支持与不确定性量化的研究者和医生。
在重症监护环境中，及时准确的预测对患者结局至关重要，尤其对于脓毒症这类需早期干预的疾病。本文提出 NeuroSep-CP-LCB，一种将神经网络与上下文多臂赌博机、共形预测相结合的新方法，用于早期脓毒症检测。不同于以往仅选择最优预训练模型的工作，该方法直接利用神经网络建模患者特异性的奖励函数，实现个性化自适应决策。通过融合神经网络的表征能力与共形预测的鲁棒性，该框架显式考虑离线数据分布的不确定性，为预测结果提供可行动的置信边界。
原文摘要 · Abstract (English)
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.
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