Dynamic Decision Making With Individualized Variable Selection

Physicians today have access to a variety of tests for diagnosing and prognosticating medical conditions. Ideally, they would apply a high-quality prediction model utilizing all relevant features to facilitate appropriate decision-making (e.g., treatment selection; risk assessment). However, some of these features incur additional costs and are not readily available to patients and physicians. In practice, predictors are typically gathered sequentially, i.e., physicians continually evaluate information dynamically until sufficient information is acquired to make a reasonable confidence decision. More importantly, the prospective information to collect may differ for each patient and depend on the predictor values already known. In this paper, we design a novel adaptive prediction rule to determine the optimal order of acquiring features in predicting a clinical outcome of interest. The objective is to maximize prediction accuracy while minimizing the cost associated with measuring features for individual subjects. To achieve this, we employ reinforcement learning, where the agent decides the best action at each step: either making a final clinical decision or continuing to collect new predictors based on the current state of knowledge. Extensive simulation studies have been conducted to evaluate the efficacy of the proposed strategy. Additionally, real examples are presented to illustrate the practical utility.

当前，临床医师可依托多种检测手段完成疾病的诊断与预后评估。理想状态下，医师会采用整合所有相关特征的高质量预测模型，以辅助制定恰当的临床决策（例如治疗方案选择、风险评估）。然而，部分特征检测会产生额外成本，且患者与医师未必能即时获取相关检测结果。实际操作中，特征信息通常按序采集：医师会动态持续评估已有信息，直至获取足够数据以做出置信度合理的临床决策。更为关键的是，针对不同患者，待采集的前瞻性信息存在差异，且取决于已获取的特征检测结果。本文设计了一种全新的自适应预测规则，用于确定在预测目标临床结局时采集特征的最优顺序，其目标是在最大化预测准确率的同时，最小化为个体受试者采集特征所产生的相关成本。为实现该目标，本文采用强化学习（reinforcement learning）方法，其中AI智能体（AI Agent）会在每一步决策最优行动：要么做出最终临床决策，要么基于当前已掌握的知识状态继续采集新的特征检测项。本文开展了大量仿真研究以评估所提策略的有效性，此外还通过实际案例展示了该策略的实际应用价值。