发表机构
Fred Hutchison Cancer Center（弗雷德·哈钦森癌症中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对临床监测中的及时风险分类问题，提出原始-对偶交替神经学习方法，采用循环神经网络和原始-对偶更新方案，在满足灵敏度和监测成本约束下最大化特异性，经模拟和临床应用验证了方法的有效性。
AI 中文摘要
及时风险分类在许多临床监测场景中至关重要，在此场景中，决策必须平衡尽早对患者分类以进行后续干预的益处，与观察更多数据的价值。然而，大多数现有的统计和机器学习方法是为完全观测的轨迹设计的，对灵敏度、特异性和监测成本等关键操作特征的控制有限。我们将序贯分类问题置于针对这三个标准的多目标优化框架中，通过值递归来表征最优决策规则，该递归在每个时间点量化即时分类与持续监测之间的权衡。为了从数据中估计该规则，我们构建了一个约束优化问题，在满足预先指定的灵敏度和监测成本约束的同时最大化特异性。随后，我们开发了一种估计方法，采用循环神经网络来近似演化的值过程，并采用原始-对偶更新方案来满足性能约束。通过模拟研究以及在低血糖风险预测的连续葡萄糖监测中的应用，我们证明所提出的方法能产生准确且及时的序贯决策规则，且符合期望的操作特征。
英文摘要
Timely risk classification is essential in many clinical monitoring settings, where decisions must balance the benefit of classifying patients early for subsequent intervention against the value of observing additional data. Yet most existing statistical and machine-learning methods are designed for fully observed trajectories and offer limited control over key operating characteristics such as sensitivity, specificity, and monitoring cost. We cast the sequential classification problem within a multi-objective optimization framework targeting these three criteria. We characterize the optimal decision rule through a value recursion that quantifies, at each time point, the trade-off between immediate classification and continued monitoring. To estimate the rule from data, we formulate a constrained optimization problem that maximizes specificity while enforcing prespecified sensitivity and monitoring-cost constraints. We then develop an estimation procedure that employs a recurrent neural network to approximate the evolving value processes and a primal--dual updating scheme to satisfy the performance constraints. Through simulation studies and an application to continuous glucose monitoring for hypoglycemia risk prediction, we demonstrate that the proposed method yields accurate and timely sequential decision rules that adhere to the desired operating characteristics.
