Breast tumor diagnosis via phrase level self-attention mechanism

In recent years there has been a surge in female Breast Cancer patients leading to an increase in the administration of Electronic Health Records (EHR) data during the treatment process. So far, mammogram examinations are the main means of breast cancer detection. The data obtained from such medical reports remains an important source for constructing AI diagnostic models for breast cancer. With a purpose of contributing and assisting doctors in clinical decision-making through provision of high-quality and efficient tumor diagnosis using AI, this paper explores and trains the breast tumor X-ray examination report, and constructs the breast tumor classification neural network (BTDNN) model to understand the auxiliary diagnosis of breast tumor. The proposed model (BTDNN) builds and uses a semantic network to define and structure the standard for breast cancer X-ray report medical entities and entity annotations which act as the standardized input of the model. Based on the semantic network, we also built a breast cancer diagnosis model based on Phrase level self-attention mechanism that uses Phrase level context technology to train the model for analysing medical reports in the same concept as a health care professional with original, local and global method context, in order to improve the prediction accuracy. As result, we compared our proposed model to several other models and concluded that combining the semantic and contextual technology could improve the final prediction accuracy of a medical diagnosis.