修剪通往最佳护理的道路:通过反向增强学习来识别系统地低于最佳的医疗决策
Inko Bovenzi1, Adi Carmel1, Michael Hu1
1Harvard University, Cambridge, MA, USA.
概括
这项研究使用反向强化学习 (IRL) 来识别在重症监护病房 (ICU) 的非最佳临床决策. 消除这些非共识性行动揭示了关键的临床优先事项和价值观,在疾病和人口统计学上产生不同的影响.
科学领域:
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
- 临床决策分析 临床决策分析
背景情况:
- 临床观察数据包含复杂的决策模式.
- 确定不理想的临床医生行动对于改善患者的治疗结果至关重要.
- 现有的方法可能很难在复杂的数据集中区分最佳和次优行为.
研究的目的:
- 应用反向强化学习 (IRL) 来分析重症监护病房 (ICU) 的临床医生决策.
- 通过将其与同行行为的比较来识别和描述非最佳的临床医生的行为.
- 从真实世界的ICU数据中发现潜在的临床优先事项和价值观.
主要方法:
- 开发了一种两阶段的IRL方法.
- 实施了一个中间步骤来修剪非共识轨迹.
- 该模型应用于观察性ICU数据,涵盖各种患者群体和疾病.
主要成果:
- 该IRL模型成功地确定了不理想的临床医生行为.
- 修剪非共识行为揭示了不同的临床优先事项.
- 消除低于最佳的行动对各种疾病的影响有所不同.
- 某些人口群体受到行动优化带来的好处的影响不成比例.
结论:
- 新的IRL应用程序有效地模拟临床决策,并识别与共识的偏差.
- 这些发现凸显了临床实践中的异质性及其对不同患者群体的影响.
- 这种方法通过了解决策细微差别,为完善临床指导方针和提高医疗保健质量提供了一条途径.
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