一种基于模型的机械通气模糊逻辑决策系统

A Fuzzy Logic Model-based Advisory System for Mechanical Ventilation

机械通气是临床上用于改善患者呼吸的重要辅助手段,所以机械通气参数设定的正确与否直接影响着患者体内的气体交换过程。本文尝试建立一种基于人体肺部气体交换数学模型的机械通气模糊逻辑决策系统。人体肺部气体交换数学模型可以根据患者的生理参数模拟出患者的肺部气体交换情况,便于临床医生更好地了解和掌握患者的病情。决策系统应用模糊控制的方法提出适合患者的机械通气设定参数,为临床医生进行机械通气的设置提供参考。实验选取10名在ICU接受机械通气治疗的患者,系统根据每一位患者的生理参数建立了相应的气体交换数学模型,并提供了适合患者的机械通气设定参数,与患者当前接受的机械通气设定参数进行对比。结果表明系统提出的机械通气设定参数在满足患者气体交换要求的前提下尽可能地降低了吸入氧浓度的值,在满足分钟通气量的前提下尽可能地降低患者呼吸功,改善了患者的气体交换情况。

In the clinical practice,the mechanical ventilation is a very important assisting method to improve the patients＇ breath.Whether or not the parameters set for the ventilator are correct would affect the pulmonary gas exchange.In this study,we try to build an advisory system based on the gas exchange model for mechanical ventilation using fuzzy logic.The gas exchange mathematic model can simulate the individual patient＇s pulmonary gas exchange,and can help doctors to learn the patient＇s exact situation.With the fuzzy logic algorithm,the system can generate ventilator settings respond to individual patient,and provide advice to the doctors.It was evaluated in 10 intensive care patient cases,with mathematic models fitted to the retrospective data and then used to simulate patient response to changes in therapy.Compared to the ventilator set only as part of routine clinical care,the present system could reduce the inspired oxygen fraction,reduce the respiratory work,and improve gas exchange with the model simulated outcome.