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A neural network model for diagnosis of critical patients receiving mechanical ventilation

机译:用于诊断重症机械通气的神经网络模型

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Artificial Neural Networks (ANNs) have become powerful tools for medical decision making. While they are able to extract decision strategies from input data, they can improve their own performance from the specific patient context. In this paper the authors present an ANN based on the PATRICIA system research work. This system has been built to obtain five categories of diagnoses for ICU patient receiving mechanical ventilation. The ANN model has been trained from 30 data spread sheets including information about ventilation, oxygenation, acid-base balance and other clinical parameters. The authors' next step will be to improve the capabilities of the system by using 60 patients under continuous monitoring.
机译:人工神经网络(ANN)已成为进行医疗决策的强大工具。尽管他们能够从输入数据中提取决策策略,但他们可以根据特定患者的情况改善自己的表现。在本文中,作者提出了一种基于PATRICIA系统研究工作的人工神经网络。建立该系统的目的是为接受机械通气的ICU患者获得五类诊断。 ANN模型已经从30个数据散布表中进行了训练,包括有关通气,充氧,酸碱平衡和其他临床参数的信息。作者的下一步将是通过在连续监测下使用60名患者来改善系统的功能。

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