Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study

Shuyan Jin; Haobin Liang; Wenxia Zhang; Huan Li

doi:10.2196/52210

Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study

JMIR Med Inform. 2024 Feb 22:12:e52210. doi: 10.2196/52210.

Authors

Shuyan Jin¹, Haobin Liang², Wenxia Zhang¹, Huan Li¹

Affiliations

¹ Health Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China.
² School of Economics and Statistics, Guangzhou University, Guangzhou, China.

PMID: 38409769
PMCID: PMC11004512
DOI: 10.2196/52210

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 aim to integrate literature data on breast cancer treatment and prevention, build a knowledge graph, and provide support for clinical decision-making.

Methods: We used Medical Subject Headings terms to search for clinical trial literature on breast cancer prevention and treatment published on PubMed between 2018 and 2022. We downloaded triplet data from the Semantic MEDLINE Database (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 5 years, malignant neoplasms appeared most frequently (587/1387, 42.3%). Pharmacotherapy (267/1387, 19.3%) was the primary treatment method, with trastuzumab (209/1805, 11.6%) being the most commonly used therapeutic drug. Through the analysis of the knowledge graph, we have discovered a complex network of relationships between treatment methods, therapeutic drugs, and preventive measures for different types of breast cancer.

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 5 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.

Keywords: adverse reaction; breast cancer; knowledge graph; prevention; treatment.