Marrero A, Méndez J A, Reboso J A, Martín I, Calvo J L
Department of Computer Science and System Engineering, Universidad de La Laguna, San Cristóbal de La Laguna, Tenerife, Spain.
Hospital Universitario de Canarias, San Cristóbal de La Laguna, Tenerife, Spain.
J Clin Monit Comput. 2017 Apr;31(2):319-330. doi: 10.1007/s10877-016-9868-y. Epub 2016 Apr 12.
This paper addresses the problem of patient model synthesis in anesthesia. Recent advanced drug infusion mechanisms use a patient model to establish the proper drug dose. However, due to the inherent complexity and variability of the patient dynamics, difficulty obtaining a good model is high. In this paper, a method based on fuzzy logic and genetic algorithms is proposed as an alternative to standard compartmental models. The model uses a Mamdani type fuzzy inference system developed in a two-step procedure. First, an offline model is obtained using information from real patients. Then, an adaptive strategy that uses genetic algorithms is implemented. The validation of the modeling technique was done using real data obtained from real patients in the operating room. Results show that the proposed method based on artificial intelligence appears to be an improved alternative to existing compartmental methodologies.
本文探讨了麻醉中患者模型合成的问题。近期先进的药物输注机制利用患者模型来确定合适的药物剂量。然而，由于患者动态特性固有的复杂性和变异性，获得一个良好模型的难度很大。本文提出了一种基于模糊逻辑和遗传算法的方法，作为标准房室模型的替代方案。该模型使用了以两步程序开发的Mamdani型模糊推理系统。首先，利用来自真实患者的信息获得一个离线模型。然后，实施一种使用遗传算法的自适应策略。使用从手术室真实患者获得的实际数据对建模技术进行了验证。结果表明，所提出的基于人工智能的方法似乎是现有房室方法的一种改进替代方案。
