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The model of health status diagnosis and evaluation of critical patients with the method of artificial neural network

机译:人工神经网络方法在危重病人健康状况诊断与评价模型中的应用

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This paper presents a model for diagnosis and evaluation of ICU patient health status based on artificial neural network. It takes the physiological parameters and seriousness grading of APACHE II scoring system as the sample data. Trainings and tests with the method of back propagation neural network classification algorithm are made to the neural network, and the result indicates that the accuracy of the model is over 85%. Contrast to the traditional methods, this model shortens the time to build a health status evaluation system of ICU and decreases the sample size demanded for the evaluation system.
机译:本文提出了一种基于人工神经网络的ICU患者健康状况诊断与评估模型。它以APACHE II评分系统的生理参数和严重性等级作为样本数据。对神经网络进行了反向传播神经网络分类算法的训练和测试,结果表明该模型的准确性在85%以上。与传统方法相比,该模型缩短了建立ICU健康状况评估系统的时间,并减少了评估系统所需的样本量。

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