Inferring diseases related to the patient’s electronic medical records (EMRs) is of great significance for assisting doctor diagnosis. Several recent prediction methods have shown that deep learning-based methods can learn the deep and complex information contained in EMRs. However, they do not consider the discriminative contributions of different phrases and words. Moreover, local information and context information of EMRs should be deeply integrated. A new method based on the fusion of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with attention mechanisms is proposed for predicting a disease related to a given EMR, and it is referred to as FCNBLA. FCNBLA deeply integrates local information, context information of the word sequence and more informative phrases and words. A novel framework based on deep learning is developed to learn the local representation, the context representation and the combination representation. The left side of the framework is constructed based on CNN to learn the local representation of adjacent words. The right side of the framework based on BiLSTM focuses on learning the context representation of the word sequence. Not all phrases and words contribute equally to the representation of an EMR meaning. Therefore, we establish the attention mechanisms at the phrase level and word level, and the middle module of the framework learns the combination representation of the enhanced phrases and words. The macro average f-score and accuracy of FCNBLA achieved 91.29 and 92.78%, respectively. The experimental results indicate that FCNBLA yields superior performance compared with several state-of-the-art methods. The attention mechanisms and combination representations are also confirmed to be helpful for improving FCNBLA’s prediction performance. Our method is helpful for assisting doctors in diagnosing diseases in patients.

中文翻译：

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基于CNN和BiLSTM的中文电子病历辅助诊断，具有短语级别和单词级别的关注。

推断与患者电子病历（EMR）相关的疾病对于协助医生诊断具有重要意义。几种最新的预测方法表明，基于深度学习的方法可以学习EMR中包含的深度和复杂信息。但是，他们没有考虑不同短语和单词的区别性贡献。此外，EMR的本地信息和上下文信息应深入整合。提出了一种基于卷积神经网络（CNN）和双向长期短期记忆（BiLSTM）融合且具有注意力机制的新方法，用于预测与给定EMR相关的疾病，该方法被称为FCNBLA。FCNBLA深度集成了本地信息，单词序列的上下文信息以及更具信息量的短语和单词。开发了一种基于深度学习的新颖框架来学习局部表示，上下文表示和组合表示。框架的左侧基于CNN构建，以学习相邻单词的本地表示。基于BiLSTM的框架的右侧侧重于学习单词序列的上下文表示。并非所有的短语和单词都对EMR含义的表达做出同等的贡献。因此，我们在短语级别和单词级别建立注意力机制，框架的中间模块学习增强的短语和单词的组合表示。FCNBLA的宏观平均f得分和准确性分别达到91.29和92.78％。实验结果表明，与几种最新方法相比，FCNBLA具有更高的性能。注意机制和组合表示也被确认有助于改善FCNBLA的预测性能。我们的方法有助于协助医生诊断患者的疾病。