基于模型的强化学习用于个性化肝素给药
Model Based Reinforcement Learning for Personalized Heparin Dosing
AI总结：
针对肝素浓度不可测且患者代谢动态差异显著的问题，提出按患者参数化的预测模型与基于场景生成的安全强化学习框架，实现个性化肝素给药。
摘要：
序贯决策中的一个关键挑战是在部分信息下安全地优化系统。尽管大量文献关注状态部分已知或动态部分已知的情形，但当状态和动态均部分已知时，这一挑战会进一步加剧。为患者计算肝素剂量正符合这一范式，因为患者体内的肝素浓度无法直接测量，而且不同个体代谢肝素的速率差异很大。虽然许多已提出的解决方案是无模型的，但它们需要复杂的模型，并且难以确保安全性。然而，如果动态的某些结构已知，就可以利用基于模型的方法来提供安全策略。本文提出了这样一个框架，以应对优化个性化肝素剂量的挑战。我们使用一个按患者单独参数化的预测模型来预测未来的治疗效果。随后，我们利用一种基于场景生成的方法来使用该模型，该方法能够确保患者安全。我们通过数值实验验证了模型：将该模型的预测能力与现有机器学习技术进行比较，并展示我们的给药算法如何能够在模拟ICU环境中治疗患者。
英文摘要：
A key challenge in sequential decision making is optimizing systems safely under partial information. While much of the literature has focused on the cases of either partially known states or partially known dynamics, it is further exacerbated in cases where both states and dynamics are partially known. Computing heparin doses for patients fits this paradigm since the concentration of heparin in the patient cannot be measured directly and the rates at which patients metabolize heparin vary greatly between individuals. While many proposed solutions are model free, they require complex models and have difficulty ensuring safety. However, if some of the structure of the dynamics is known, a model based approach can be leveraged to provide safe policies. In this paper we propose such a framework to address the challenge of optimizing personalized heparin doses. We use a predictive model parameterized individually by patient to predict future therapeutic effects. We then leverage this model using a scenario generation based approach that is capable of ensuring patient safety. We validate our models with numerical experiments by comparing the predictive capabilities of our model against existing machine learning techniques and demonstrating how our dosing algorithm can treat patients in a simulated ICU environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
