The clinical notes are usually typed into the system by physicians. They are typically required to be marked by standard medical codes, and each code represents a diagnosis or medical treatment procedure. Annotating these notes is time consuming and prone to error. In this paper, we proposed a multi-view attention based Neural network to predict medical codes from clinical texts. Our method incorporates three aspects of information, the semantic context of the clinical text, the relationship among the label (medical codes) space, and the alignment between each pair of a clinical text and medical code. Our method is verified to be effective on the open source dataset. The experimental result shows that our method achieves better performance against the prior state-of-art on multiple metrics.

翻译：临床记录通常由医生录入系统，并需按标准医学编码进行标注，每个编码对应一项诊断或医疗操作流程。手工标注这些记录既耗时又容易出错。本文提出一种基于多视角注意力的神经网络模型，用于从临床文本中预测医学编码。该方法融合了三方面信息：临床文本的语义上下文、标签（医学编码）空间内的关联关系，以及每对临床文本与医学编码之间的对齐信息。我们在开源数据集上验证了该方法的有效性。实验结果表明，在多项评估指标上，本方法均优于现有最先进技术。