Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and brings great pressure to the development of the medical industry. The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network. The establishment of the system can effectively improve the automation and intelligence of modern psychiatric treatment process. In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed. Combined with the medical knowledge base, the analysis of disease results is realized, and on this basis, the efficacy of related drugs in the treatment of mental diseases is evaluated. The results show that the advantage of the deep convolution neural network system is to effectively improve the induction rate. What’s more, compared with other algorithms, this algorithm has higher accuracy and efficiency. It improves the comprehensiveness and informatization of disease screening methods, improves the accuracy of screening, reduces the consumption of doctors’ human resources, and provides a theoretical basis for the digitization of the medical industry in the future.

中文翻译：

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基于灰色关联分析优化的卷积神经网络系统构建及药物评价

随着城市的发展，精神疾病的发病率逐年上升。现代医学数据量巨大且复杂。很多情况下很难实现资源的合理配置，这对现代医学的人工智能提出了迫切的需求，也给医疗行业的发展带来了巨大的压力。本研究的目的是开发并构建基于深度卷积神经网络的精神疾病灰色关联分析及相关药物评价系统。该系统的建立可以有效提高现代精神科治疗过程的自动化和智能化。本文对患者数据进行灰色关联分析，然后构建优化的深度卷积神经网络。结合医学知识库，实现疾病结果分析，并在此基础上评估相关药物治疗精神疾病的疗效。结果表明，深度卷积神经网络系统的优势在于有效提高归纳率。更重要的是，与其他算法相比，该算法具有更高的精度和效率。提高了疾病筛查方法的综合性和信息化，提高了筛查的准确性，减少了医生人力资源的消耗，为未来医疗行业的数字化提供了理论基础。