论文标题
使用基于分区的标签注意的自动ICD编码网络
An Automatic ICD Coding Network Using Partition-Based Label Attention
论文作者
论文摘要
国际疾病分类(ICD)是一种全球医学分类系统,为适合患者临床记录的诊断和程序提供了独特的代码。但是,人类编码人员的手动编码昂贵且容易出错。自动ICD编码有可能解决此问题。随着深度学习技术的发展,正在开发许多用于自动ICD编码的深度学习方法。特别是,标签注意机制对于多标签分类有效,即ICD编码。它有效地从输入临床记录中获得了标签特异性表示。但是,由于现有的标签注意机制立即找到整个文本中的关键令牌,因此可以从注意图中省略每个段落中的重要信息。为了克服这一点,我们提出了一种新型的神经网络架构,由编码器的两个部分和两种标签的注意层组成。输入文本在以前的编码器中进行了分段编码,并由追随者集成。然后,常规和基于分区的标签注意机制提取了重要的全球和局部特征表示。我们的分类器有效地集成了它们,以增强ICD编码性能。我们使用Mimic-III(ICD编码的基准数据集)验证了提出的方法。我们的结果表明,我们的网络基于基于分区的机制提高了ICD编码性能。
International Classification of Diseases (ICD) is a global medical classification system which provides unique codes for diagnoses and procedures appropriate to a patient's clinical record. However, manual coding by human coders is expensive and error-prone. Automatic ICD coding has the potential to solve this problem. With the advancement of deep learning technologies, many deep learning-based methods for automatic ICD coding are being developed. In particular, a label attention mechanism is effective for multi-label classification, i.e., the ICD coding. It effectively obtains the label-specific representations from the input clinical records. However, because the existing label attention mechanism finds key tokens in the entire text at once, the important information dispersed in each paragraph may be omitted from the attention map. To overcome this, we propose a novel neural network architecture composed of two parts of encoders and two kinds of label attention layers. The input text is segmentally encoded in the former encoder and integrated by the follower. Then, the conventional and partition-based label attention mechanisms extract important global and local feature representations. Our classifier effectively integrates them to enhance the ICD coding performance. We verified the proposed method using the MIMIC-III, a benchmark dataset of the ICD coding. Our results show that our network improves the ICD coding performance based on the partition-based mechanism.