This paper is in the following e-collection/theme issue:

Theme issue 2019: Health Natural Language Processing (17) Electronic Health Records (1068) Natural Language Processing (706)

Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17645, first published .
A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development

A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development

A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development

Authors of this article:

Linfeng Li1, 2 Author Orcid Image ;   Peng Wang3, 4 Author Orcid Image ;   Yao Wang2 Author Orcid Image ;   Shenghui Wang1 Author Orcid Image ;   Jun Yan2 Author Orcid Image ;   Jinpeng Jiang2 Author Orcid Image ;   Buzhou Tang5 Author Orcid Image ;   Chengliang Wang3 Author Orcid Image ;   Yuting Liu6 Author Orcid Image
Article Authors Cited by (16) Tweetations (13) Metrics

Journals

  1. Hao T, Huang Z, Liang L, Weng H, Tang B. Health Natural Language Processing: Methodology Development and Applications. JMIR Medical Informatics 2021;9(10):e23898 View
  2. Li M, Huang K, Zitnik M. Graph representation learning in biomedicine and healthcare. Nature Biomedical Engineering 2022;6(12):1353 View
  3. Wu X, Duan J, Pan Y, Li M. Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications. Big Data Mining and Analytics 2023;6(2):201 View
  4. He Q, Liu S, Liu Y. Optimal Recommendation Models Based on Knowledge Representation Learning and Graph Attention Networks. IEEE Access 2023;11:19809 View
  5. Suo X, Guo B, Shen Y, Dai S, Wang W, Chen Y, Zhang Z. KRL_Match: knowledge graph objects matching for knowledge representation learning. Knowledge and Information Systems 2023;65(2):641 View
  6. Cui Z, Chen H, Cui L, Liu S, Liu X, Xu G, Yin H. Reinforced KGs reasoning for explainable sequential recommendation. World Wide Web 2022;25(2):631 View
  7. Liu X, Wang H, He T, Liao Y, Jian C. Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review. Journal of Physics: Conference Series 2022;2188(1):012007 View
  8. Cho H, Ahn I, Gwon H, Kang H, Kim Y, Seo H, Choi H, Kim M, Han J, Kee G, Jun T, Kim Y. Heterogeneous graph construction and HinSAGE learning from electronic medical records. Scientific Reports 2022;12(1) View
  9. Himmelstein D, Zietz M, Rubinetti V, Kloster K, Heil B, Alquaddoomi F, Hu D, Nicholson D, Hao Y, Sullivan B, Nagle M, Greene C. Hetnet connectivity search provides rapid insights into how biomedical entities are related. GigaScience 2022;12 View
  10. Hoang V, Nguyen T, Lee S, Lee J, Nguyen L, Lee O. Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning. IEEE Access 2023;11:114238 View
  11. Yang P, Wang H, Huang Y, Yang S, Zhang Y, Huang L, Zhang Y, Wang G, Yang S, He L, Huang Y. LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications. Knowledge-Based Systems 2024;284:111323 View
  12. Liu C, Li Z, Li J, Qu Y, Chang Y, Han Q, Cao L, Lin S. Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation. JMIR Medical Informatics 2024;12:e55090 View

Books/Policy Documents

  1. Guluzade A, Kacupaj E, Maleshkova M. Artificial Intelligence in Medicine. View
  2. Zhou Y, Wang Z, Li M, Wu J. Health Information Processing. View