修剪通往最优护理的路径：利用逆强化学习识别系统性次优医疗决策
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
- Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种基于逆强化学习的框架，通过两阶段IRL和轨迹修剪，从ICU观察数据中识别次优临床决策，并发现移除次优行动的效果因疾病和人口群体而异。
AI中文摘要：
本研究旨在从临床环境的观察数据中揭示医疗决策的内在见解，我们提出了一种逆强化学习（IRL）的新应用，基于同行医生的行为来识别次优的临床医生行动。该方法以两阶段IRL为核心，并在中间步骤修剪那些行为显著偏离共识的轨迹。这使我们能够有效地从包含最优和次优临床医生决策的ICU数据中识别临床优先事项和价值观。我们观察到，移除次优行动的好处因疾病而异，并对某些人口群体产生差异化影响。
英文摘要：
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actions of their peers. This approach centers two stages of IRL with an intermediate step to prune trajectories displaying behavior that deviates significantly from the consensus. This enables us to effectively identify clinical priorities and values from ICU data containing both optimal and suboptimal clinician decisions. We observe that the benefits of removing suboptimal actions vary by disease and differentially impact certain demographic groups.
