Accurate classification of tumor text based on attention mechanism and transfer learning

With the increase in the number of electronic medical records, precise classification of medical records can be use to accurately diagnose diseases. In medical texts, there are differences in the number of texts of different diseases, especially for specific diseases and a small number of samples have brought great challenges to medical text classification. In response to this problem, this paper proposes an accurate text classification model base on transfer learning combined with attention mechanism neural network, and finally obtains accurate category text through two-step classification. The model first uses the attention mechanism long-term and short-term cyclic neural network to extract the overall tumor sample from the mass of unbalanced medical record texts through the common characteristics of the tumor medical record, and uses the similarity between diseases to combine with the convolutional neural network through migration learning. The characteristics of each type of tumor disease are migrate to achieve the effect of precise classification training. The precise training model is used to finally realize the precise classification of tumor diseases. The model was tested on a private tumor medical data set, and the results showed that the accuracy of the tumor data set (precision) was increased by 5% compare to the average of the baseline optimal classification model. The increase in accuracy is important for accurate classification significance.