基于标签注意力的分层ICD自动编码方法

Hierarchical automated ICD encoding method based on label attention

针对目前自动ICD(international classification of diseases)编码任务存在标签空间大、诊断代码分布不均衡与临床文本表征差的问题,提出一种融合Longformer与标签注意力的分层ICD自动编码模型。借助Clinical-Longformer预训练语言模型获得融合长文本语境的词向量表征。通过将标签的语义表示与注意力机制相结合,捕捉临床文本中与诊断代码相关的关键特征信息,获取更精准的文本表示。引入分层联合学习机制,建立分层预测层解码输出ICD编码。实验结果表明,该模型的准确率、召回率与F1值均高于现有模型,验证了该方法进行自动ICD编码的有效性,为实施疾病诊断相关分组提供高质量的数据支撑。

Aiming at the problems of large label space,unbalanced distribution of diagnostic codes and poor representation of clinical texts in the current automated ICD(international classification of diseases)coding task,a hierarchical automated ICD coding model integrating Longformer and label attention was proposed.With the help of Clinical-Longformer pre-trained language model,the word vector representation fused with the context of the long text was obtained.By combining the semantic representation of the labels with the attention mechanism,the key feature information related to the diagnostic codes in the clinical text was captured,and a more accurate textual representation was obtained.A hierarchical joint learning mechanism was introduced,and a hierarchical prediction layer was established to decode the output ICD codes.Experimental results show that the accuracy,recall and F1 value of the model are higher than that of the existing models,which verifies the effectiveness of the method for automated ICD coding.The method provides high-quality data support for the implementation of diagnosis related groups.