用逆强化学习识别临床决策中的低效行为，发现不同疾病和人群影响各异。
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
- 通过逆强化学习分析医生行为，筛选出偏离主流的异常决策路径。
- 去除低效操作后，不同疾病治疗效果提升程度存在差异。
- 适用于医疗质量评估与个性化诊疗优化研究者。
为从临床观察数据中挖掘医疗决策隐含规律，我们提出一种新颖的逆强化学习（Inverse Reinforcement Learning, IRL）应用，通过对比医生群体行为，识别出偏离共识的次优临床决策。该方法包含两个阶段的IRL，并引入中间修剪步骤，剔除显著偏离主流行为的决策轨迹。由此可从包含最优与次优决策的ICU数据中有效提取临床优先级与价值判断。研究发现，移除次优行为的收益因疾病类型而异，且对不同人口学群体的影响不均。结果揭示了医疗决策中的系统性偏差及其潜在影响。
原文摘要 · Abstract (English)
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.
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