Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome

Suyuan Peng, Yadan Fan, Liwei Wang, Andrew Wen, Xusheng Liu, Hongfang D Liu, Feichen Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The purpose of this study is to explore pathophysiological mechanisms present in patients that suffer from Chronic Kidney Disease complicated by Metabolic Syndrome (CKD-MetS) so as to better support proactive treatment. Association rule mining was applied to extract significant associations from the Semantic MEDLINE Database (SemMedDB). A total of 23,310 PMIDs with 5,542 unique items were included in our dataset. We focused on 5 specific syndromes that were extracted: diabetes, cardiovascular disease, increased triglycerides, obesity and inflammation. The number of rules generated for these five diseases are SO, 197, 31, 21 and 21 respectively. Our study identified several pathophysiological mechanisms that exist in CKD-MetS patients that can contribute to further renal damage.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1302-1309
Number of pages8
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

Keywords

  • Association rule mining
  • CKD-MetS
  • Semantic MEDLINE Database

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Peng, S., Fan, Y., Wang, L., Wen, A., Liu, X., Liu, H. D., & Shen, F. (2019). Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1302-1309). [8621226] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621226