JMIR Publications

ABSTRACT

Background:

The incidence of breast cancer has remained high and continues to rise since the 21st century. Consequently, there has been a significant increase in research efforts focused on breast cancer prevention and treatment. Despite the extensive body of literature available on this subject, systematic integration is lacking. To address this issue, knowledge graphs have emerged as a valuable tool. By harnessing their powerful knowledge integration capabilities, knowledge graphs offer a comprehensive and structured approach to understanding breast cancer prevention and treatment.

Objective:

We aimed to build a knowledge graph to integrate information related to breast prevention and treatment.

Methods:

We used MESH terms to search for clinical trial literature on breast cancer prevention and treatment published on PubMed between 2018 and 2021. We downloaded triplet data from SemmedDB and matched them with the retrieved literature to obtain triplet data for the target articles. We visualized the triplet information using NetworkX for knowledge discovery.

Results:

Within the scope of literature research in the past five years, Malignant neoplasm appeared most frequently (42.32%). Pharmacotherapy (19.22%) was the primary treatment method, with Paclitaxel (9.83%) being the most commonly used therapeutic drug. Through the analysis of the knowledge graph, we have discovered that there exists a complex network relationship between the treatment methods, therapeutic drugs, and preventive measures for different types of breast cancer, rather than a simple linear correlation.

Conclusions:

This study constructed a knowledge graph for breast cancer prevention and treatment, which enabled the integration and knowledge discovery of relevant literature in the past five years. Researchers can gain insights into treatment methods, drugs, preventive knowledge regarding adverse reactions to treatment, and the associations between different knowledge domains from the graph.