Ratajczyk Pawel, Dominikowski Bartosz, Czylkowska Agnieszka, Rogalewicz Bartlomiej, Kulak Cezary, Gaszynski Tomasz
Department of Anaesthesiology and Intensive Therapy, Medical University of Lodz, Lodz, Poland.
Institute of Electrical Engineering Systems, Lodz University of Technology, Lodz, Poland.
Med Devices (Auckl). 2024 Oct 26;17:401-415. doi: 10.2147/MDER.S483837. eCollection 2024.
The aim of the article is to determine the appropriate concentration of desflurane to effectively counteract the increase in blood pressure resulting from surgical stress. In medical practice, this increase is often limited by using additional doses of opioid drugs. Additional medications or higher doses of those already used may adversely affect your health. During anesthesia, physician must note the use of drugs and remember them, especially those that he has recently administered, which affect his concentration. For this purpose, the authors decided to propose support for the selection of desflurane concentration so that frequent use of opioid drugs is not necessary. The authors used a system based on AI issues to accomplish this task. The learned system supports the anesthesiologist's work by imitating him.
The proposed method for selecting the desflurane concentration is based on a fuzzy controller. This system includes a learning mechanism that allows for minimizing the operating error. The main advantage of this system is the ability to build a function allowing the selection of anesthesia parameters without knowledge of the mathematical description of the process. To accomplish this task, you need an expert who will provide information in the construction of logical if-then sentences (points in space). The fuzzy controller connects the points in the consideration space appropriately, generating a hypersurface. The algorithm test was performed only by computer without the participation of patients.
The operation of the proposed algorithm was verified by computer simulation. The authors of the article analyzed the compliance of the obtained results with the table provided by the expert. The desflurane concentration values obtained by computer simulation are similar to those given in the table Minimal driver error does not affect the patient's clinical response. This error results from the functions used in the fuzzy system and its settings. The results of the performance test of the proposed algorithm are presented in a time course, and it has the shape of a step function. The work proposes a function that allows you to enter the time needed for the body's reaction to reach the desired level.
In this study, a controller was created to support the selection of the concentration of desflurane allowing for a reduction in blood pressure (resulting from surgical stress). The results obtained by computer simulation provide valuable insights for optimizing anesthesia. This system can also be used as an important simulation program for teaching purposes.
本文的目的是确定地氟醚的合适浓度，以有效抵消手术应激导致的血压升高。在医学实践中，这种血压升高通常通过使用额外剂量的阿片类药物来限制。额外的药物或已使用药物的更高剂量可能会对健康产生不利影响。在麻醉期间，医生必须注意药物的使用并记住它们，尤其是他最近使用的那些会影响其注意力的药物。为此，作者决定提出一种支持选择地氟醚浓度的方法，这样就无需频繁使用阿片类药物。作者使用了一个基于人工智能问题的系统来完成这项任务。该学习系统通过模仿麻醉医生的工作来提供支持。
所提出的选择地氟醚浓度的方法基于一个模糊控制器。该系统包括一个学习机制，可使操作误差最小化。该系统的主要优点是能够构建一个函数，在无需了解过程数学描述的情况下选择麻醉参数。要完成此任务，需要一位专家在构建逻辑“如果 - 那么”语句（空间中的点）时提供信息。模糊控制器将考虑空间中的点适当地连接起来，生成一个超曲面。该算法测试仅通过计算机进行，没有患者参与。
所提出算法的运行通过计算机模拟进行了验证。本文作者分析了所得结果与专家提供的表格的符合程度。通过计算机模拟获得的地氟醚浓度值与表格中的值相似。最小驱动误差不会影响患者的临床反应。此误差源于模糊系统中使用的函数及其设置。所提出算法的性能测试结果以时间进程呈现，其形状为阶跃函数。该研究提出了一个函数，可用于输入身体反应达到期望水平所需的时间。
在本研究中，创建了一个控制器来支持地氟醚浓度的选择，以降低（由手术应激导致的）血压。计算机模拟获得的结果为优化麻醉提供了有价值的见解。该系统还可作为教学用途的重要模拟程序。
