Enhancing ophthalmology medical record management with multi-modal knowledge graphs

The electronic medical record management system plays a crucial role in clinical practice, optimizing the recording and management of healthcare data. To enhance the functionality of the medical record management system, this paper develops a customized schema designed for ophthalmic diseases. A mul...

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Bibliographic Details
Published in:Scientific reports 2024-10, Vol.14 (1), p.23221-12, Article 23221
Main Authors: Gao, Weihao, Rong, Fuju, Shao, Lei, Deng, Zhuo, Xiao, Daimin, Zhang, Ruiheng, Chen, Chucheng, Gong, Zheng, Niu, Zhiyuan, Li, Fang, Wei, Wenbin, Ma, Lan
Format: Article
Language:English
Subjects:
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Algorithms
Diagnosis
Disease management
Electronic Health Records
Electronic medical records
Eye diseases
Eye Diseases - diagnosis
Eye Diseases - therapy
Health care
Healthcare
Humanities and Social Sciences
Humans
Knowledge graph
Knowledge representation
Medical record management
Medical records
multidisciplinary
Ophthalmology
Ophthalmology - methods
Patients
Science
Science (multidisciplinary)
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Summary:The electronic medical record management system plays a crucial role in clinical practice, optimizing the recording and management of healthcare data. To enhance the functionality of the medical record management system, this paper develops a customized schema designed for ophthalmic diseases. A multi-modal knowledge graph is constructed, which is built upon expert-reviewed and de-identified real-world ophthalmology medical data. Based on this data, we propose an auxiliary diagnostic model based on a contrastive graph attention network (CGAT-ADM), which uses the patient’s diagnostic results as anchor points and achieves auxiliary medical record diagnosis services through graph clustering. By implementing contrastive methods and feature fusion of node types, text, and numerical information in medical records, the CGAT-ADM model achieved an average precision of 0.8563 for the top 20 similar case retrievals, indicating high performance in identifying analogous diagnoses. Our research findings suggest that medical record management systems underpinned by multimodal knowledge graphs significantly enhance the development of AI services. These systems offer a range of benefits, from facilitating assisted diagnosis and addressing similar patient inquiries to delving into potential case connections and disease patterns. This comprehensive approach empowers healthcare professionals to garner deeper insights and make well-informed decisions.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-73316-9