用注意力TCN提升病历编码准确率，尤其大幅改善召回率。
Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding
- 分层时序卷积+标签专属注意力，精准捕捉长文本信息
- 相比顶尖模型F1提升9%，召回率飙升28%
- 适合临床决策支持系统，重视漏诊避免的场景
医疗编码是根据住院记录文本为每次住院分配诊断和操作代码的任务，需整合文本各部分信息并针对不同代码关注不同内容，对专业编码员也极具挑战。本文将其建模为多标签文本分类问题。提出一种深层神经网络模型，包含多层时序卷积网络（TCN）和标签专属注意力机制。多层TCN能提取长序列全局表示，学习远距离依赖；标签专属注意力则使模型在处理每个代码时可聚焦文档不同方面。实验显示，该方法相较现有最佳模型，F1分数提升9%，召回率显著提高28%，我们认为在临床决策支持场景中，召回率更为关键。
原文摘要 · Abstract (English)
Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts of the text and focus to different sections for each individual code, making it a very difficult problem even for professional human coders. We model the task as a multi-label text classification problem. To overcome the mentioned difficulties, we propose a deep neural model consisting of a multi-layer temporal convolution network (TCN) followed by label-wise attention. While multi-layer TCN helps extract a global document representation with the ability to learn relations over very long sequences, label-specific attention mechanism allows the model to focus on different aspects of the same document for each individual label. Our method achieves significantly better F-1 scores (9% increase) compared to the previous state-of-the-art model, with a remarkable increase in recall score (28% increase), which we believe is the more important metric for a clinical decision support setting.
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