Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study
Open accessAbstract
With the growth of data and intelligent technologies, the healthcare sector opened numerous technology that enabled services for patients, clinicians, and researchers. One major hurdle in achieving state-of-the-art results in health informatics is domain-specific terminologies and their semantic complexities. A knowledge graph crafted from medical concepts, events, and relationships acts as a medical semantic network to extract new links and hidden patterns from health data sources. Current medical knowledge graph construction studies are limited to generic techniques and opportunities and focus less on exploiting real-world data sources in knowledge graph construction. A knowledge graph constructed from Electronic Health Records (EHR) data obtains real-world data from healthcare records. It ensures better results in subsequent tasks like knowledge extraction and inference, knowledge graph completion, and medical knowledge graph applications such as diagnosis predictions, clinical recommendations, and clinical decision support. This review critically analyses existing works on medical knowledge graphs that used EHR data as the data source at (i) representation level, (ii) extraction level (iii) completion level. In this investigation, we found that EHR-based knowledge graph construction involves challenges such as high complexity and dimensionality of data, lack of knowledge fusion, and dynamic update of the knowledge graph. In addition, the study presents possible ways to ta…
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Knowledge graph technology has been increasingly applied in education to represent complex knowledge systems through structured networks.
“In parallel, knowledge graph technology has been increasingly applied in education to represent complex knowledge systems through structured networks [12, 13].”
Knowledge graphs provide a structured visual representation of relationships among concepts, enabling students to organize knowledge more effectively.
“Knowledge graphs provide a structured visual representation of relationships among concepts, enabling students to organize knowledge more effectively [12, 14].”
The results highlight the importance of combining AI technologies with structured knowledge representation tools, such as knowledge graphs.
“Second, the results highlight the importance of combining AI technologies with structured knowledge representation tools, such as knowledge graphs [12, 13, 14].”
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Harnessing EHR data for health research
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“Knowledge graphs provide a structured visual representation of relationships among concepts, enabling students to organize knowledge more effectively [12, 14].”
- Authors
- Murali, L., Gopakumar, G., Viswanathan, D. M., & Nedungadi, P.
- Affiliations
- Cochin University of Science and Technology (IN)
- Amrita Vishwa Vidyapeetham (IN)
- Corresponding author
- Nedungadi, Prema
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 143
- Pages
- 104403
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W4377695655
- Cited by
- 93
- References
- 9
- Citations by year
- 2026: 24 · 2025: 37 · 2024: 26 · 2023: 6
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 366 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- medical knowledge graph, electronic health records, knowledge graph construction, knowledge graph completion, knowledge extraction, knowledge fusion, clinical decision support, diagnostic prediction, medical terminology, knowledge graph integration, knowledge graph updating
- MeSH terms
- Delivery of Health Care, Humans, Pattern Recognition, Automated, Decision Support Systems, Clinical, Knowledge Bases, Electronic Health Records
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