基于门控卷积和注释-代码交互的医学代码分配
Medical Code Assignment with Gated Convolution and Note-Code Interaction
摘要：
从临床文本中分配医疗编码是临床信息系统管理的一项基础性工作。由于医疗记录通常很长，医疗编码系统的代码空间很大，因此这项任务是一个长期的挑战。最近的工作应用深度神经网络模型对医疗笔记进行编码，并将医疗代码分配给临床文档。然而，这些方法仍然是无效的，因为它们没有完全编码和捕获冗长和丰富的语义信息的医疗笔记，也没有明确地利用笔记和代码之间的相互作用。我们提出了一种新的方法，门控卷积神经网络，和一个音符代码交互（GatedCNN-NCI），自动医疗代码分配，以克服这些挑战。我们的方法捕获丰富的语义信息的冗长的临床文本更好地表示，通过利用嵌入注入和门控信息传播的医疗笔记编码模块。通过一种新颖的笔记-代码交互设计和图形消息传递机制，我们显式地捕获笔记和代码之间的潜在依赖关系，从而实现有效的代码预测。为了减少可训练参数的数量，还设计了一种权重分配方案。在真实世界临床数据集上的实证实验表明，我们提出的模型在大多数情况下优于最先进的模型，并且我们的模型大小与轻量基线相当。
英文摘要：
Medical code assignment from clinical text is a fundamental task in clinical information system management. As medical notes are typically lengthy and the medical coding system's code space is large, this task is a long-standing challenge. Recent work applies deep neural network models to encode the medical notes and assign medical codes to clinical documents. However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes. We propose a novel method, gated convolutional neural networks, and a note-code interaction (GatedCNN-NCI), for automatic medical code assignment to overcome these challenges. Our methods capture the rich semantic information of the lengthy clinical text for better representation by utilizing embedding injection and gated information propagation in the medical note encoding module. With a novel note-code interaction design and a graph message passing mechanism, we explicitly capture the underlying dependency between notes and codes, enabling effective code prediction. A weight sharing scheme is further designed to decrease the number of trainable parameters. Empirical experiments on real-world clinical datasets show that our proposed model outperforms state-of-the-art models in most cases, and our model size is on par with light-weighted baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
