A visualization algorithm for medical big data based on deep learning

This paper proposes a visualization algorithm model based on deep learning. First, through entity link disambiguation and knowledge map embedding and extraction, the structured disease knowledge in the medical knowledge map is obtained, and the disease feature word vector and the corresponding knowledge entity vector in the disease description text are used as the multi-channel input of Convolutional Neural Networks (CNN). Second, in the convolution process, different types of diseases are represented from two levels of semantics and knowledge. Finally, through training and testing on multiple types of disease description text data sets. The experimental results show that the sensitivity of the method is 0.94, and the accuracy rate reaches 0.87, which verifies that this method of joint training of knowledge and data is more suitable for the initial diagnosis of the disease type described by the condition.