发表机构
Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究医学编码难题，提出由多层时间卷积网络和标签明智注意力组成的深度神经模型，相比之前模型，F-1分数显著提高，召回分数大幅增加，对临床决策支持有重要意义。
AI 中文摘要
医学编码是一项根据记录的笔记为住院治疗分配一组诊断和程序代码的任务。它需要汇总文本不同部分的信息，并针对每个单独的代码关注不同的部分，即使对于专业的人类编码人员来说，这也是一个非常困难的问题。我们将该任务建模为多标签文本分类问题。为克服上述困难，我们提出了一种深度神经模型，该模型由多层时间卷积网络（TCN）和标签明智注意力组成。多层TCN有助于提取全局文档表示并学习长序列关系，标签特定的注意力机制使模型能够针对每个单独标签关注同一文档的不同方面。我们的方法与之前的最先进模型相比，F-1分数显著提高（提高了9%），召回分数显著增加（提高了28%），我们认为召回分数在临床决策支持设置中是更重要的指标。
英文摘要
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.
CommentsWork carried out in 2019; posted as a record of the work. Baselines and state of the art reflect the 2019 literature
