| 标题 |
 EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation 基于多尺度特征注意和结构化知识图传播的EHR编码
相关领域
计算机科学
杠杆（统计）
卷积神经网络
医学分类
编码（社会科学）
医学诊断
机器学习
领域知识
特征学习
人工智能
自然语言处理
数学
医学
统计
病理
护理部
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| 网址 | |
| DOI | |
| 其它 | Assigning standard medical codes (e.g., ICD-9-CM) representing diagnoses or procedures to electronic health record (EHR) is an important task in the medical domain. However, automatic coding is difficult since the clinical note is composed of multiple long and heterogeneous textual narratives (e.g., discharge diagnosis, pathology reports, surgical procedure notes). Furthermore, the code label space is large and the label distribution is extremely unbalanced. The state-of-the-art methods mainly regard EHR coding as a multi-label text classification task and use shallow convolution neural network with fixed window size, which is incapable of learning variable n-gram features and the ontology structure between codes. In this paper, we leverage a densely connected convolutional neural network which is able to produce variable n-gram features for clinical note feature learning. |
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