Clinical notes are comprehensive files containing explicit information about a patient's visit. However, accurately assigning medical codes from clinical documents can be a persistent challenge due to the complexity of clinical data and the vast range of medical codes. Moreover, the large volume of medical records, the noisy medical records, and the uneven quality of coders all negatively impact the quality of the final codes. Deep learning technology has recently been integrated into automatic International Classification of Diseases (ICD) coding tasks to improve accuracy. Nevertheless, the imbalanced class problem, the complexness of code associations, and the noise in lengthy records still restrict the advancement of ICD coding tasks in deep learning. Thus, we present the Note-code Interaction Denoising Network (NIDN) that employs the self-attention mechanism to pull critical semantic features in electronic medical records (EMRs). Our model utilizes the label attention mechanism for retaining code-specific text expression. We introduce Clinical Classifications Software coding for multitask learning, capturing the functional relationships of medical coding to oblige in model prediction. To minimize the impact of noise on model prediction and improve the label distribution imbalance, a denoising module is introduced to filter noise. Our practical consequences indicate that the model NIDN exceeds competitive models on a third version of Medical Information Mart for Intensive Care data set.

中文翻译：

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通过带有去噪机制的注释代码交互网络进行自动国际疾病分类编码。

临床记录是包含有关患者就诊的明确信息的综合文件。然而，由于临床数据的复杂性和广泛的医疗代码，从临床文档中准确分配医疗代码可能是一个持续的挑战。此外，大量的病历、嘈杂的病历以及编码人员的质量参差不齐都会对最终代码的质量产生负面影响。深度学习技术最近已被集成到自动国际疾病分类（ICD）编码任务中，以提高准确性。尽管如此，类不平衡问题、代码关联的复杂性以及冗长记录中的噪声仍然限制了深度学习中ICD编码任务的进展。因此，我们提出了注释代码交互去噪网络（NIDN），它采用自注意力机制来提取电子病历（EMR）中的关键语义特征。我们的模型利用标签注意机制来保留特定于代码的文本表达。我们引入了用于多任务学习的临床分类软件编码，捕获医学编码的功能关系以促进模型预测。为了最小化噪声对模型预测的影响并改善标签分布不平衡，引入去噪模块来过滤噪声。我们的实际结果表明，NIDN 模型在第三版重症监护医疗信息集市数据集上超过了竞争模型。