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) Secondary Use of Clinical Data for Research and Surveillance (375)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17653, first published .
Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation

Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation

Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation

Authors of this article:

Haixia Sun1 Author Orcid Image ;   Jin Xiao2 Author Orcid Image ;   Wei Zhu2 Author Orcid Image ;   Yilong He2 Author Orcid Image ;   Sheng Zhang2 Author Orcid Image ;   Xiaowei Xu1 Author Orcid Image ;   Li Hou1 Author Orcid Image ;   Jiao Li1 Author Orcid Image ;   Yuan Ni2 Author Orcid Image ;   Guotong Xie2 Author Orcid Image
Article Authors Cited by (18) Tweetations (1) Metrics

Journals

  1. Abu-Salih B. Domain-specific knowledge graphs: A survey. Journal of Network and Computer Applications 2021;185:103076 View
  2. 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
  3. Yang Y, Cao Z, Zhao P, Zeng D, Zhang Q, Luo Y. Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study. Journal of Safety Science and Resilience 2021;2(3):146 View
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  5. Villegas-Ortega J, Bellido-Boza L, Mauricio D. Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review. Health & Justice 2021;9(1) View
  6. Binsfeld Gonçalves L, Nesic I, Obradovic M, Stieltjes B, Weikert T, Bremerich J. Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame. JMIR Medical Informatics 2022;10(12):e40534 View
  7. Rajabi E, Etminani K. Knowledge-graph-based explainable AI: A systematic review. Journal of Information Science 2024;50(4):1019 View
  8. 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
  9. Li J, Lan Q, Zhu E, Xu Y, Zhu D. A Study of Health Insurance Fraud in China and Recommendations for Fraud Detection and Prevention. Journal of Organizational and End User Computing 2022;34(4):1 View
  10. Abu-Salih B, AL-Qurishi M, Alweshah M, AL-Smadi M, Alfayez R, Saadeh H. Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities. Journal of Big Data 2023;10(1) View
  11. Zhang Y, Xiao G. A novel customizing knowledge graph evaluation method for incorporating user needs. Scientific Reports 2024;14(1) View
  12. Park C, Lee H, Jeong O. Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction. Future Internet 2024;16(8):260 View
  13. ZHANG Z, TAN C, ZHAO X, YANG M. Type-Enhanced Ensemble Triple Representation via Triple-Aware Attention for Cross-Lingual Entity Alignment. IEICE Transactions on Information and Systems 2024;E107.D(9):1182 View
  14. El-Enen M, Tbaishat D, Sahlol A, Nazir A, AlMaymun K, AbdulRazek M, Muhammad R, Adlan F, Sharma R. Fraud Detection in Medical Insurance Claims Using Majority Voting of Multiple Unsupervised Algorithms. Procedia Computer Science 2024;244:9 View

Books/Policy Documents

  1. Zhu W, Li W, Wang X, Ji W, Wu Y, Chen J, Chen L, Tang B. Health Information Processing. Evaluation Track Papers. View
  2. Datta V, Ganesh S, Haas R, Talukder A. Big Data and Artificial Intelligence. View