Wang Yongyan, Ma Songhua, Hu Tianliang, Ma Dedong, Lian Xianhui, Wang Shuai, Zhang Jiguo
School of Mechanical Engineering, Shandong University, Jinan 250061, P. R. China.
Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan 250061, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Oct 25;40(5):945-952. doi: 10.7507/1001-5515.202209015.
The setting and adjustment of ventilator parameters need to rely on a large amount of clinical data and rich experience. This paper explored the problem of difficult decision-making of ventilator parameters due to the time-varying and sudden changes of clinical patient's state, and proposed an expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment based on fuzzy control rule and neural network. Based on the method and the real-time physiological state of clinical patients, we generated a mechanical ventilation decision-making solution set with continuity and smoothness, and automatically provided explicit parameter adjustment suggestions to medical personnel. This method can solve the problems of low control precision and poor dynamic quality of the ventilator's stepwise adjustment, handle multi-input control decision problems more rationally, and improve ventilation comfort for patients.
呼吸机参数的设置与调整需要依赖大量临床数据和丰富经验。本文探讨了由于临床患者状态的时变和突变导致呼吸机参数决策困难的问题,并提出了一种基于专家知识、基于模糊控制规则和神经网络的呼吸机参数设置及无级自适应调整策略。基于该方法和临床患者的实时生理状态,生成了具有连续性和平滑性的机械通气决策解集,并自动向医务人员提供明确的参数调整建议。该方法能够解决呼吸机逐步调整控制精度低、动态品质差的问题,更合理地处理多输入控制决策问题,提高患者通气舒适度。