Enhancing Ophthalmology Medical Record Management with Multi-modal Knowledge Graphs

Overview

Journal Sci Rep

Specialty Science

Date 2024 Oct 5

PMID 39369079

Authors

Weihao Gao

Fuju Rong

Lei Shao

Zhuo Deng

Daimin Xiao

Ruiheng Zhang

Chucheng Chen

Zheng Gong

Zhiyuan Niu

Fang Li

Wenbin Wei

Lan Ma

Affiliations

Soon will be listed here.

Abstract

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.

References

Burton M, Ramke J, Marques A, Bourne R, Congdon N, Jones I . The Lancet Global Health Commission on Global Eye Health: vision beyond 2020. Lancet Glob Health. 2021; 9(4):e489-e551. PMC: 7966694. DOI: 10.1016/S2214-109X(20)30488-5. View

Hasan S, Rivera D, Wu X, Durbin E, Christian J, Tourassi G . Knowledge Graph-Enabled Cancer Data Analytics. IEEE J Biomed Health Inform. 2020; 24(7):1952-1967. PMC: 8324069. DOI: 10.1109/JBHI.2020.2990797. View

Si Y, Du J, Li Z, Jiang X, Miller T, Wang F . Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review. J Biomed Inform. 2021; 115:103671. PMC: 11290708. DOI: 10.1016/j.jbi.2020.103671. View

Zheng W, Yan L, Gou C, Zhang Z, Jason Zhang J, Hu M . Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis. Inf Fusion. 2021; 75:168-185. PMC: 8168340. DOI: 10.1016/j.inffus.2021.05.015. View

Li R, Yin C, Yang S, Qian B, Zhang P . Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach. J Med Internet Res. 2020; 22(9):e20645. PMC: 7551124. DOI: 10.2196/20645. View

Shang Y, Tian Y, Zhou M, Zhou T, Lyu K, Wang Z . EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice. IEEE J Biomed Health Inform. 2021; 25(7):2463-2475. DOI: 10.1109/JBHI.2021.3085003. View

Li L, Wang P, Yan J, Wang Y, Li S, Jiang J . Real-world data medical knowledge graph: construction and applications. Artif Intell Med. 2020; 103:101817. DOI: 10.1016/j.artmed.2020.101817. View

Shickel B, Tighe P, Bihorac A, Rashidi P . Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE J Biomed Health Inform. 2018; 22(5):1589-1604. PMC: 6043423. DOI: 10.1109/JBHI.2017.2767063. View

Pokharel S, Zuccon G, Li X, Utomo C, Li Y . Temporal tree representation for similarity computation between medical patients. Artif Intell Med. 2020; 108:101900. DOI: 10.1016/j.artmed.2020.101900. View

10.

Sun Z, Lu X, Duan H, Li H . Deep Dynamic Patient Similarity Analysis: Model Development and Validation in ICU. Comput Methods Programs Biomed. 2022; 225:107033. DOI: 10.1016/j.cmpb.2022.107033. View

11.

Slater K, Karwath A, Williams J, Russell S, Makepeace S, Carberry A . Towards similarity-based differential diagnostics for common diseases. Comput Biol Med. 2021; 133:104360. PMC: 8204262. DOI: 10.1016/j.compbiomed.2021.104360. View

12.

Gu Y, Yang X, Tian L, Yang H, Lv J, Yang C . Structure-aware siamese graph neural networks for encounter-level patient similarity learning. J Biomed Inform. 2022; 127:104027. DOI: 10.1016/j.jbi.2022.104027. View