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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >A study on weaning results of ventilator-dependent patients with an artificial neuromolecular system
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A study on weaning results of ventilator-dependent patients with an artificial neuromolecular system

机译：人工神经分子系统对呼吸机依赖患者的断奶结果的研究

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摘要

Ventilator has been widely used to support the breathing needs of patients, and weaning is the process of removing ventilator from them. So far there is no positive answer about whether it will be successful to wean a patient off a ventilator. It may be of help if we develop an intelligent system to assist clinicians in making such a decision. In this paper, we apply an artificial neuromolecular system (ANM system), which is a selforganizing learning system, to a database of 189 weaned patients. The ANM system is a multi-level evolutionary learning architecture that captures the gradual transformability feature of structure-function relationship embedded in biological systems. Our experiments with the model show that the integrated system achieves a satisfactory result in separating those patients who have successful weaning results from those who do not, based on the 27 parameters that may affect their weaning results. Our parameter analysis shows that most of the parameters identified as significant by the system are the same as those by clinicians, but some are not. The finding of the latter should provide clinicians another dimension of information, in particular the effectiveness of each parameter in determining weaning results for patients.
机译：呼吸机已被广泛用于满足患者的呼吸需求，而断奶是从患者身上移除呼吸机的过程。迄今为止，尚无关于将患者从呼吸机上撤下来是否成功的肯定答案。如果我们开发一个智能系统来帮助临床医生做出这样的决定，可能会有所帮助。在本文中，我们将人工神经分子系统（ANM系统）（一种自组织的学习系统）应用于189名断奶患者的数据库。 ANM系统是一个多级进化学习体系结构，它捕获了嵌入生物系统中的结构-功能关系的逐步可转换性特征。我们使用该模型进行的实验表明，基于可能影响断奶结果的27个参数，该集成系统将成功断奶的患者与未成功断奶的患者分离开来，取得了令人满意的结果。我们的参数分析表明，系统确定为重要的大多数参数与临床医生相同，但有些则不相同。后者的发现应为临床医生提供另一个方面的信息，尤其是在确定患者断奶结果时每个参数的有效性。

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