Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;
Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;
Graduate School of Engineering, University of Hyogo, Himeji, Hyogo, Japan;
Japan Institute for Nursing Quality Improvement (JINQI), Akashi, Hyogo, Japan;
Japan Institute for Nursing Quality Improvement (JINQI), Akashi, Hyogo, Japan;
Convolutional neural networks; Feature extraction; Hospitals; Numerical models; Predictive models;
机译:卷积神经网络(CNN)和递归神经网络(RNN)架构在放射学文本报告分类中的比较有效性
机译:文本情绪分类的可变卷积与汇集卷积神经网络
机译:将上下文相关概念纳入卷积神经网络以进行短文本分类
机译:使用卷积神经网络分析护理文本评估的分类结果
机译:基于卷积神经网络和递归神经网络的深度神经语言文本分类模型
机译:具有基于规则的功能和知识导向的卷积神经网络的临床文本分类
机译:卷积神经网络(CNN)和反复性神经网络(RNN)架构对放射学文本报告分类的比较有效性