Constructing a knowledge graph for health intervention in hypertension from the perspective of precise health management

GAO Ya; CHEN Ning; YUAN Ling; CHEN Hong; TAN Shuoyuan; HE Yibei; WU Qingyu; HOU Hongpan; WANG Zhaoxin; SHI Jianwei

doi:10.16766/j.cnki.issn.1674-4152.003292

Volume 21 Issue 12

Dec. 2023

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Article Navigation > Chinese Journal of General Practice > 2023 > 21(12): 2068-2073

GAO Ya, CHEN Ning, YUAN Ling, CHEN Hong, TAN Shuoyuan, HE Yibei, WU Qingyu, HOU Hongpan, WANG Zhaoxin, SHI Jianwei. Constructing a knowledge graph for health intervention in hypertension from the perspective of precise health management[J]. Chinese Journal of General Practice, 2023, 21(12): 2068-2073. doi: 10.16766/j.cnki.issn.1674-4152.003292

Citation:

GAO Ya, CHEN Ning, YUAN Ling, CHEN Hong, TAN Shuoyuan, HE Yibei, WU Qingyu, HOU Hongpan, WANG Zhaoxin, SHI Jianwei. Constructing a knowledge graph for health intervention in hypertension from the perspective of precise health management[J]. Chinese Journal of General Practice, 2023, 21(12): 2068-2073. doi: 10.16766/j.cnki.issn.1674-4152.003292

Citation:

GAO Ya, CHEN Ning, YUAN Ling, CHEN Hong, TAN Shuoyuan, HE Yibei, WU Qingyu, HOU Hongpan, WANG Zhaoxin, SHI Jianwei. Constructing a knowledge graph for health intervention in hypertension from the perspective of precise health management[J]. Chinese Journal of General Practice, 2023, 21(12): 2068-2073. doi: 10.16766/j.cnki.issn.1674-4152.003292

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Constructing a knowledge graph for health intervention in hypertension from the perspective of precise health management

doi: 10.16766/j.cnki.issn.1674-4152.003292

1.
School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Funds:

2022YFC3601505

22692107200

Received Date: 2023-09-20
Available Online: 2024-01-29

Abstract

Abstract

Objective Primary healthcare institutions face significant challenges in managing hypertension, necessitating practical and effective tools. Therefore, this study aims to construct a knowledge graph for hypertension health intervention offering valuable insights for precise health intervention. Methods A systematic search for hypertension related clinical guidelines and expert consensuses resulted in 5 Chinese guidelines, 3 Chinese expert consensus, and 9 English guidelines. Utilizing these guidelines and expert consensuses as the knowledge source for health interventions in hypertensive patients, a comprehensive list to form the initial batch of entities. Subsequently, an ontology framework was developed, encompassing three age groups (children and adolescents, adults, and the elderly), multiple coexisting diseases, special types of hypertension, and four health intervention modules (nutrition, exercise, psychology, and sleep). Based on the ontology framework, knowledge extraction was performed on the list of health intervention items, adhering to the principles of entity and attribute element extraction. The resulting annotated entity triples formed the data layer. Following knowledge fusion, the Neo4j graph database was used to store and display entity triples, ultimately generating a comprehensive knowledge graph was ultimately generated which can provide corresponding health intervention plans for various types of hypertension patients. Results The constructed knowledge graph of health intervention for hypertension visualizes the textual content derived from guidelines and expert consensus. It encompasses refined information on hypertension patients and offers specific intervention plans. This knowledge graph serves as a valuable tool for family physicians, enabling them to conduct long-term health intervention with greater convenience and precision for all the patients. Conclusion The knowledge graph of health inventions for hypertension provides an effective path for family physicians to access health intervention plans. It serves as a valuable resource for guiding community practice, providing effective guidance and support.
- Hypertension,
- Health intervention,
- Health management,
- Family physician,
- Knowledge graph

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References(24)

References

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