Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

中文翻译：

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基于卷积神经网络的电子病历临床辅助诊断

从电子病历中自动提取有用信息以及进行疾病诊断对于临床决策支持（CDS）和神经语言处理（NLP）都是一项有前途的任务。现有的大多数系统都是基于人工构建的知识库，然后通过规则匹配来完成辅助诊断。在这项研究中，我们提出一种基于卷积神经网络（CNN）的临床智能决策方法，该方法可以自动提取电子病历的高级语义信息，然后执行自动诊断，而无需人工构建规则或知识库。我们使用收集的18,590份真实世界的临床电子病历来训练和测试所提出的模型。实验结果表明，该模型可以达到98。