Towards autonomous management of cardiogenic shock

迈向心源性休克的自主管理

基本信息

批准号：
10376242
负责人：
Nicholas E. Houstis
金额：
$ 25.2万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10376242
关键词：
Algorithms Artificial Intelligence Automation Aviation Awareness Behavior Benchmarking Cardiac Cardiogenic Shock Cardiovascular Models Cardiovascular Physiology Caring Cessation of life Clinical Clinical Data Cognitive Complex Computerized Medical Record Critical Illness Data Data Set Databases Decision Making Devices Dose Electronic Health Record Environment Evaluation Event Family Feedback Functional disorder Future General Hospitals Goals Growth Hand Head Heart failure Human Intensive Care Intensive Care Units Intervention Knowledge Lead Learning Life Massachusetts Measurement Mechanics Methods Modeling Monitor Nature Operative Surgical Procedures Organ failure Outcome Oxygen Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Phase Physicians Physiology Policies Psyche structure Psychological reinforcement Publishing Quality of Care Rewards Robotics Role Safety Sampling Self-Help Devices Suggestion System Techniques Technology Telemetry Time Titrations Training Work algorithm training animation base cardiac intensive care unit comorbidity computer science computerized tools design digital health experience hemodynamics improved individualized medicine learning algorithm learning strategy patient response preservation simulation support tools tool unethical

项目摘要

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.

项目摘要心脏重症监护病房（CCU）的医师在越来越多的数据和数据中做出决定知识丰富的世界，但他们经常获得的帮助。目前，每个身体都根据他的或她对患者生理学的心理模型，以及对患者对患者的反应的心理预测干涉。这种方法可以导致一系列行为，以损害患者的结果，包括过度简化生理学，由于认知超负荷引起的错误以及身体变异性决策。具有定量知识的计算工具，能够系统地评估所有数据，并通过过去的行动事件数据库告知，可以帮助物理学有宝贵的行动建议。我们建议培训一种算法，以决定给药血管活性药物并启动由于心力衰竭而导致心脏病性休克患者的机械支持。这套集中的决策需要计算通常在物理学头上进行的生理学。我们框架决策问题是优化心血管功能以保存氧气的功能，我们将使用工具最佳控制。而不是手动设计CCU控制器，我们将使用加固学习（RL）技术 “适合”一个。在过去的几年中，RL领域经历了爆炸性的增长，并取得了显着进步战略决策问题和机器人技术。临床环境中的主要挑战是勘探阶段在实际患者中，学习（“试验和错误”）将是不道德的。第二个挑战是可用性患者数据虽然成长，但可能是瓶颈。我们将利用最先进的模型RL训练使用仿真和从历史数据中学习的算法结合使用的算法。我们将使用模型心血管生理学的基础，该生理是当今用于心脏病学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除了所有的标准电子病历数据外，还记录了实时遥测波形数据 CCU患者跨越数年。这是同类数据集中最大，最完整的数据集之一。这随着工具变得越来越复杂，管理心源性冲击的复杂性将继续升级患者寿命更长，合并症更广泛。高级决策支持工具可以帮助驯服复杂性，提高护理质量以及使其民主化。