Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations.

中文翻译：

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使用专家系统为心肺管理和重症监护室镇静提供临床决策支持和闭环控制

由于急性呼吸衰竭而需要机械通气的重症监护病房 (ICU) 患者也经常需要使用镇静剂。镇静的需要来自患者因失去个人控制和 ICU 陌生和侵入性环境而产生的焦虑，以及疼痛或其他有害刺激的变体。当医生选择用于镇静和心血管功能的药物时，这些药物的实际给药是护理人员的责任。如果可以开发临床决策支持系统和闭环控制系统用于重症监护监测和救生干预以及镇静和心肺管理的管理，ICU护士可以从镇静的密集监测中解脱出来，让她/他可以专注于其他关键任务。一种特别有吸引力的策略是利用熟练临床医生的知识和经验，明确掌握专家临床医生用于根据镇静水平决定如何滴定药物剂量的规则。在本文中，我们通过使用概率论来量化不确定性，从而将用于心肺管理和 ICU 镇静框架的确定性基于规则的专家系统扩展到随机设置，从而处理更现实的临床情况。