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.
翻译:国际疾病分类(疾病分类)是一个全球医疗分类系统,它为诊断和适合病人临床记录的程序的诊断和程序提供了独特的代码,然而,人类编码员的人工编码费用昂贵,而且容易出错。自动的 ICD编码有可能解决这个问题。随着深层次学习技术的进步,许多基于深层次学习的ICD自动编码方法正在得到开发。特别是,标签注意机制对多标签分类有效,即ICD编码。它从输入的临床记录中有效地获得特定标签的表述。然而,由于现有的标签注意机制一次性发现整个文本中的关键符号,因此每个段落中散布的重要信息可能从关注图中省略。为了克服这个问题,我们建议建立一个由两个编码器和两种标签注意层组成的新型神经网络结构。输入文本在前编码器中进行了部分编码,并由后续者加以整合。然后,基于常规和分区的标签注意机制提取重要的全球和本地特征表示。我们的分类机制有效地整合了它们,以便从整个文本中找到关键符号,每个段落中散布的重要信息可能会被忽略。为了克服这个问题,我们建议了一个由两个编码器组成的神经网络组成的新网络结构结构结构结构。我们用ICD改进了一种业绩。我们提议的ICD的IMIA的计算结果。我们用I-I-I-I-I-I-I-I-I-I-I-I-I-ISD-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-C-C-MI-I-I-CO-MA-MA-BD-BD-S-SD-I-I-BD-I-I-I-I-I-I-I-MA-MA-MA-I-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-CO-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-MA-