EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Open accessAbstract
Objective Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient.Methods We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance.Results As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level.Conclusion Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions.
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GNNs can leverage relational structures such as similarities among chest X-rays to improve disease diagnosis and localization.
“GNNs are also shown to leverage relational structures like similarities among chest X-rays to improve downstream tasks, such as disease diagnosis and localization [198].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [198]
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Self-citation
- Authors
- Zhao, C., Jiang, J., Guan, Y., Guo, X., & He, B.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Guan, Yi
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 87
- Pages
- 49-59
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2964200294
- Cited by
- 41
- References
- 63
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 5 · 2022: 9 · 2021: 6 · 2020: 9 · 2019: 5 · 2018: 1
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Harbin Medical University
- Second Affiliated Hospital of Harbin Medical University
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, medical knowledge representation, Markov random fields, clinical decision support, bipartite subgraphs, test recommendations, knowledge representation learning, energy function, medical entity extraction
- MeSH terms
- Machine Learning, Algorithms, Markov Chains, Likelihood Functions, Decision Support Systems, Clinical, Electronic Health Records
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