论文标题
深度强化学习是否准备好用于医疗保健中的实际应用？对败血症患者血液动力学管理的决斗-DDQN的敏感性分析
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients
论文作者
论文摘要
通过成功应用于GO和Atari等游戏，已证明了增强学习（RL）的潜力。但是，尽管可以通过简单地使用它来玩游戏来评估RL算法的性能是很简单的，但是在临床环境中，评估是一个主要的挑战，在临床环境中，在实践中遵循RL政策可能是不安全的。因此，了解RL政策对实施过程中做出的许多决定的敏感性是建立最终临床摄取所需的RL信任类型的重要一步。在这项工作中，我们对最先进的RL算法（Duel Double Deep Q-Networks）进行了灵敏度分析，该算法应用于ICU中的化粪池患者的血液动力学稳定治疗策略。我们考虑到学到的政策对输入特征，嵌入模型架构，时间离散化，奖励功能和随机种子的敏感性。我们发现，改变这些设置可以显着影响学到的政策，这表明在解释RL代理输出时需要谨慎。
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks)applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, embedding model architecture, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output.
