Coulter Department of Biomedical Engineering, Georgia Insitute of Technology, Atlanta, USA.
Department of Electrical and Computer Engineering, Georgia Insitute of Technology, Atlanta, USA.
BMC Med Inform Decis Mak. 2024 Sep 28;24(1):275. doi: 10.1186/s12911-024-02606-z.
Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization.
We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization.
The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making.
This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.
学习决策策略，如在临床环境中推荐治疗方案，对于增强临床决策支持系统至关重要。然而，挑战在于如何准确地评估和优化这些策略以实现最大疗效。本文通过关注策略学习的两个关键方面——评估和优化，解决了这一差距。
我们开发了实用临床应用的反事实策略学习算法，以建议患者可行的治疗方案。我们首先设计了一种用于反事实评估和政策增强的自举方法，旨在减少临床决策中的不确定性。在此基础上，我们引入了一种受自举原理启发的创新对抗学习算法，以进一步推进政策优化。
我们的算法在半合成和真实临床数据集上进行了验证。与基线算法相比，我们的方法将策略评估的方差降低了 30%，错误率降低了 25%。在政策优化方面，它将奖励提高了 1%至 3%，突出了我们在临床决策中的方法的实际价值。
本研究表明，在临床决策支持中，结合自举和对抗学习技术进行策略学习是有效的。它不仅提高了策略评估和优化的准确性和可靠性，而且为在医疗保健中利用先进的反事实机器学习开辟了道路。
