论文标题
临床文本的医学法规分配的卷积注意力网络扩张
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
论文作者
论文摘要
医学法规分配可以从临床文本中预测医疗法规，是智能医疗信息系统的一项基本任务。自然语言处理中深层模型的出现增强了自动分配方法的发展。但是，最近具有平坦卷积或多通道特征串联的高级神经体系结构忽略了文本序列中的顺序因果约束，并且可能无法学习有意义的临床文本表示，尤其是对于具有长期顺序依赖性的冗长临床注释。本文提出了一个扩张的卷积注意网络（DCAN），将扩张的卷积，残留连接和标签注意集成为医学法规分配。它采用了扩张的卷积，以捕获复杂的医疗模式，并具有接收场，随着扩张尺寸的呈指数增加。从经验上讲，对现实世界中临床数据集的实验表明，我们的模型改善了最新技术的状态。
Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.
