Cao Lingyu, Gu Dazhong, Ni Yuan, Xie Guotong
Ping An Health Technology, Shanghai, China.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:417-424. eCollection 2019.
Medical records are text documents recording diagnoses, symptoms, examinations, etc. They are accompanied by ICD codes (International Classification of Diseases). ICD is the bedrock for health statistics, which maps human condition, injury, disease etc. to codes. It has enormous financial importance from public health investment to health insurance billing. However, assigning codes to medical records normally needs a lot of human labour and is error-prone due to its complexity. We present a 3-layer attentional convolutional network based on the hierarchy structure of ICD code that predicts ICD codes from medical records automatically. The method shows high performance, with Hit@1 of 0.6969, and Hit@5 of 0.8903, which is better than state-of-the-art method.
医疗记录是记录诊断、症状、检查等的文本文件。它们附有ICD编码（国际疾病分类）。ICD是健康统计的基石，它将人类状况、损伤、疾病等映射到编码。从公共卫生投资到医疗保险计费，它都具有巨大的财务重要性。然而，给医疗记录分配编码通常需要大量人力，并且由于其复杂性容易出错。我们基于ICD编码的层次结构提出了一种三层注意力卷积网络，该网络可自动从医疗记录中预测ICD编码。该方法表现出高性能，命中@1为0.6969，命中@5为0.8903，优于现有方法。
