A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development
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Abstract
Background: Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. Objective: We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. Methods: We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. Results: Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. Conclusions: The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.
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TDA can generate graphs of whole-slide images including tissues from various patient sources, and GNNs can classify the stage of colon cancer.
“Alternatively, TDA can generate graphs of whole-slide images, which include tissues from various patient sources (SI Note 3), and GNNs are then used to classify the stage of colon cancer [199].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [199]
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- Authors
- Li, L., Wang, P., Wang, Y., Wang, S., Yan, J., Jiang, J., Tang, B., Wang, C., & Liu, Y.
- Affiliations
- Beijing Jiaotong University (CN)
- Chongqing University (CN)
- Harbin Institute of Technology (CN)
- Corresponding author
- Liu, Yuting
- ORCID
- Venue
- JMIR Medical Informatics
- Publisher
- JMIR Publications Inc.
- Published
- 2020-05-21
- Volume
- 8
- Issue
- 5
- Pages
- e17645
- ISSN
- 2291-9694 (ISSN-L)
- PMC
- PMC7273238
- OpenAlex
- W3012073743
- Cited by
- 25
- References
- 12
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 1
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Informatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- knowledge graph embedding, medical knowledge graph, margin-based loss, link prediction, electronic health records
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