Construction and Application Analysis of Coronary Heart Disease Medical Technology Innovation Knowledge Graph Based on Deep Learning

Coronary Heart Disease (CHD) is one of the leading causes of death and disability worldwide with high incidence and mortality rates. With the aging population and lifestyle changes, the number of CHD cases is increasing annually while posing significant public health challenges. This study constructs a knowledge graph of technological innovation in the medical field of CHD by using deep learning techniques to help medical researchers efficiently understand the current research status and trends in CHD technological innovation. The study employs the BERT model architecture with integrating the CasRel model and the Chinese-RoBERTa-WWM-EXT model for entity and relationship extraction, the F1 score of the final model reached 83%. The knowledge graph is visualized by using the Neo4j graph database, displaying patent information, technological hotspots, related institutions and personnel in the CHD field, which provides a structured knowledge platform for medical researchers. The results indicate that the deep learning-based knowledge graph construction has significant application value in the field of CHD medical technology innovation.