Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data

Abstract

The task of accurately identifying medical entities and extracting entity relationships from large-scale medical text data has become a hot topic in recent years, aiming to mine potential rules and knowledge. How to conduct in-depth context analysis from biomedical texts, such as medical procedures, diseases, therapeutic drugs, and disease characteristics, and identify valuable knowledge in the medical field is our main research content. Through the process of knowledge mining, a deeper understanding of the complex relationships between various factors in diseases can be gained, which holds significant guiding implications for clinical research. An approach based on context semantic analysis is proposed to realize medical entity recognition and entity relationship extraction. In addition, we build a medical knowledge base related to coronary heart disease and combine the NCBI disease dataset and the medical lexicon dataset extracted from the text as the test data of the experiment. Experimental results show that this model can effectively identify entities in medical text data; the WBC model achieved an F1 score of 89.2% in the experiment, while the CSR model achieved an F1 score of 83.4%, and the result is better than other methods.

Keywords

• knowledge extraction
• contextual analysis
• semantic analysis
• data mining
• NLP