BACKGROUND Diagnosis aims to predict the future health status of patients according to their historical electronic health records (EHR), which is an important yet challenging task in healthcare informatics. Existing diagnosis prediction approaches mainly employ recurrent neural networks (RNN) with attention mechanisms to make predictions. However, these approaches ignore the importance of code descriptions, i.e., the medical definitions of diagnosis codes. We believe that taking diagnosis code descriptions into account can help the state-of-the-art models not only to learn meaning code representations, but also to improve the predictive performance, especially when the EHR data are insufficient. METHODS We propose a simple, but general diagnosis prediction framework, which includes two basic components: diagnosis code embedding and predictive model. To learn the interpretable code embeddings, we apply convolutional neural networks (CNN) to model medical descriptions of diagnosis codes extracted from online medical websites. The learned medical embedding matrix is used to embed the input visits into vector representations, which are fed into the predictive models. Any existing diagnosis prediction approach (referred to as the base model) can be cast into the proposed framework as the predictive model (called the enhanced model). RESULTS We conduct experiments on two real medical datasets: the MIMIC-III dataset and the Heart Failure claim dataset. Experimental results show that the enhanced diagnosis prediction approaches significantly improve the prediction performance. Moreover, we validate the effectiveness of the proposed framework with insufficient EHR data. Finally, we visualize the learned medical code embeddings to show the interpretability of the proposed framework. CONCLUSIONS Given the historical visit records of a patient, the proposed framework is able to predict the next visit information by incorporating medical code descriptions.

中文翻译：

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纳入医疗代码描述以进行医疗保健中的诊断预测。

背景技术诊断的目的是根据患者的历史电子健康记录（EHR）来预测患者未来的健康状况，这是医疗信息学中一项重要但具有挑战性的任务。现有的诊断预测方法主要采用具有注意机制的循环神经网络（RNN）来进行预测。然而，这些方法忽略了代码描述的重要性，即诊断代码的医学定义。我们相信，考虑诊断代码描述可以帮助最先进的模型不仅学习意义代码表示，而且可以提高预测性能，特别是当 EHR 数据不足时。方法我们提出了一个简单但通用的诊断预测框架，其中包括两个基本组件：诊断代码嵌入和预测模型。为了学习可解释的代码嵌入，我们应用卷积神经网络（CNN）对从在线医疗网站提取的诊断代码的医学描述进行建模。学习到的医疗嵌入矩阵用于将输入访问嵌入到向量表示中，然后将其输入到预测模型中。任何现有的诊断预测方法（称为基本模型）都可以作为预测模型（称为增强模型）融入到所提出的框架中。结果我们在两个真实的医学数据集上进行了实验：MIMIC-III 数据集和心力衰竭索赔数据集。实验结果表明，增强诊断预测方法显着提高了预测性能。此外，我们在 EHR 数据不足的情况下验证了所提出框架的有效性。最后，我们将学习到的医疗代码嵌入可视化，以显示所提出的框架的可解释性。结论 鉴于患者的历史就诊记录，所提出的框架能够通过结合医疗代码描述来预测下次就诊信息。