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With the rapid development of Internet medical information technology, a large amount of medical data appeared on the Internet, however, how to extract effective information from the massive and complex medical data to provide professional medical services and suggestions to users has become a hot spot for this research. The recommendation system can effectively solve the problem of accurate matching of complex medical data resources; however, the cold start, data sparsity and user interest migration of the system in the complex data environment have a large impact on the recommendation effect; therefore, this paper proposes a weighted neural matrix decomposition improved health management recommendation scheme incorporating deep learning techniques. The scheme first uses an implicit feedback method to improve the prediction scores and improve the linear model performance of the matrix decomposition algorithm to form a weighted neural matrix decomposition health management recommendation algorithm. Second, the improved method and deep neural network are fused to improve the performance of the nonlinear model part of the algorithm by using the structural properties of the neural network. Finally, this paper's method is compared with the mainstream six recommendation algorithms on four publicly available real datasets. The experimental results show that the root mean square error (RMSE) of the WENMF algorithm is smaller than that of the comparison algorithm on all four datasets, and the convergence speed is faster. The hit rate (HR) and normalized discounted cumulative gain (NDCG) of the WENMF algorithm are higher than those of the comparison algorithm on all four datasets, and the maximum difference is 0.04. Therefore, the recommendation accuracy and ranking quality of the WENMF algorithm in the recommendation system are verified, and the cold start and data sparsity problems of the recommendation system are effectively alleviated. [ABSTRACT FROM AUTHOR]

Traditional treatment research lays much emphasis on therapeutic effects of a single drug or combination drugs, failing to explore appropriate treatment regimens that can be formed based on the state of an illness, medical history, financial capacity, and adverse drug reactions. For this reason, the extension innovation method is introduced to implement doctors' thinking process in formulating treatment regimens in deep learning. First, an extension model is established for diseases according to the basic-element theory and the extension set theory based on the magnetic resonance image classification result under deep learning. Subsequently, extension analysis is made to analyze pathogenesis and corresponding treatment procedures; and multiple feasible treatment regimens are generated through extension transformation. At last, priority-degree evaluation is carried out to quantitatively assess the proposed treatment regimens and select a better regime from them. Here, ankylosing spondylitis (AS) is taken for example to validate the feasibility of applying the extension innovation method in treatment regime generation. Main contributions of this research are that both the extension model and the priority-degree evaluation method are introduced in treatment regime generation. In this way, after the deep learning method was used to automatically extract and classify medical image data features and determine the disease activity stage of ankylosing spondylitis, a formal and quantitative effective method can be provided for establishing the framework model of diseases and evaluating corresponding therapeutic regimens, then a foundation can be laid for arithmetic research on intelligent treatment regime generation via computer simulation of human thinking. [ABSTRACT FROM AUTHOR]