Abstract
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.
Highlights
In recent years, with the development of science and technology, many breakthroughs have been made in the field of deep learning
The scope of computer identification data is limited and can only be applied to structured data. erefore, we need to process the massive data of mental diseases to construct an evaluation system that is widely used in the analysis of mental illness
Ere is a huge space for in-depth learning and development in the medical industry, especially in the assessment of mental illness. erefore, the purpose of this study is to develop and construct a grey relational analysis of mental illness and evaluation system of related drugs based on deep convolution neural network. e establishment of this system can effectively improve the automation and intelligence of modern psychiatric treatment process
Summary
With the development of science and technology, many breakthroughs have been made in the field of deep learning. The grey correlation analysis of the patient data was carried out and construction of the optimized depth convolution neural network was done, combined with the medical knowledge base, to achieve the analysis of the results of the disease, and on this basis, to evaluate the effect of the relevant drugs for the treatment of mental diseases. E advantage of the construction of deep convolution neural network system in this study is that it effectively improves the induction, classification, and analysis efficiency of modern mental diseases using the algorithm, makes the disease screening means more comprehensive and informative, improves the screening accuracy, and reduces the consumption of doctors’ human resources, which provides a theoretical basis for the future digitization of the medical industry The grey correlation analysis of the patient data was carried out and construction of the optimized depth convolution neural network was done, combined with the medical knowledge base, to achieve the analysis of the results of the disease, and on this basis, to evaluate the effect of the relevant drugs for the treatment of mental diseases. e advantage of the construction of deep convolution neural network system in this study is that it effectively improves the induction, classification, and analysis efficiency of modern mental diseases using the algorithm, makes the disease screening means more comprehensive and informative, improves the screening accuracy, and reduces the consumption of doctors’ human resources, which provides a theoretical basis for the future digitization of the medical industry
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