Towards autonomous management of cardiogenic shock

项目摘要 心脏重症监护病房(CCU)的医生在越来越多的数据中做出决定-并且 在知识丰富的世界里，他们往往得不到什么帮助。目前，每个医生都根据自己的情况做出决定。 她对病人生理的心理模型，以及对病人反应的心理预测 干预。这种方法可能会导致一系列损害患者预后的行为，包括 生理学过于简单化，认知负荷过重导致的错误，以及医生与医生之间的差异 做决定。一种配备了生理学定量知识的计算工具，能够 系统地评估所有数据，并由过去行动-结果事件的数据库提供信息，可以帮助 医生对行动提出了宝贵的建议。 我们建议训练一个算法来做出关于血管活性药物的剂量和启动的决定 失代偿性心力衰竭心源性休克患者的机械支持。这是一组专注于 决策需要对生理学进行计算，这通常是在医生的头脑中进行的。我们框定 作为优化心血管功能以保持氧气输送的决策问题，我们应用了来自 最优控制。我们将使用强化学习(RL)技术，而不是手动设计CCU控制器 去“合身”一件。RL领域在过去几年经历了爆炸性的增长，在以下方面取得了显著进展 战略决策问题和机器人学。临床环境中的一个关键挑战是探索阶段 在真实的病人身上，学习(“试错”)是不道德的。第二个挑战是， 患者数据虽然在增长，但很可能成为瓶颈。我们将利用最先进的基于模型的RL进行培训 一种结合使用模拟和从历史数据进行非策略学习的算法。我们将使用一个模型 心血管生理学是当今用于培训心脏病专家的心脏模拟器的基础。 历史患者数据将来自马萨诸塞州综合医院临床数据动画中心，该中心 除了标准的电子病历数据外，还记录了来自 CCU患者跨越数年。这是同类数据中最大、最完整的数据集之一。这个 随着工具变得越来越复杂，管理心源性休克的复杂性将继续升级 患者寿命更长，有更广泛的合并症。高级决策支持工具可以帮助驯服这种情况 复杂性，提高护理质量，并使其民主化。

Project Summary Physicians in the cardiac intensive care unit (CCU) make decisions in an increasingly data- and knowledge- rich world, yet often they get little help. Currently, each physician makes decisions based on his or her mental model of the patient’s physiology, together with mental predictions of the patient’s response to intervention. This approach can lead to a range of behaviors that compromise patient outcomes, including oversimplification of the physiology, errors due to cognitive overload, and physician to physician variability in decision making. A computational tool equipped with quantitative knowledge of physiology, the ability to systematically evaluate all the data, and informed by a database of past action-outcome events could aid the physician with valuable suggestions for action. We propose to train an algorithm to make decisions about dosing vasoactive medications and initiating mechanical support in patients with cardiogenic shock due to decompensated heart failure. This focused set of decisions entails calculations about the physiology that are normally performed in a physician’s head. We frame the decision problem as optimizing cardiovascular function to preserve oxygen delivery, and we apply tools from optimal control. Rather than hand-design a CCU controller we will use reinforcement learning (RL) techniques to “fit” one. The field of RL has experienced explosive growth over the past few years, with notable advances in strategic decision problems and robotics. A key challenge in the clinical environment is that the exploration phase of learning (“trial and error”) would be unethical in real patients. A second challenge is that the availability of patient data, while growing, is likely to be a bottleneck. We will leverage state-of-the-art model-based RL to train an algorithm using a combination of simulation and off-policy learning from historical data. We will use a model of cardiovascular physiology that underlies cardiac simulators in use today for the training of cardiologists. Historical patient data will come from the Massachusetts General Hospital Clinical Data Animation Center which has recorded real-time telemetry waveform data in addition to standard electronic medical record data from all CCU patients spanning several years. This is one of the largest and most complete datasets of its kind. The complexity of managing cardiogenic shock will continue to escalate as tools become more sophisticated and patients live longer, with more extensive comorbidities. Advanced decision support tools could help tame this complexity, improving the quality of care as well as democratizing it.