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

1 Citation (Scopus)

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

Fingerprint

Association rules
Chronic Renal Insufficiency
Semantics
MEDLINE
Triglycerides
Cardiovascular Diseases
Medical problems
Obesity
Databases
Inflammation
Kidney
Therapeutics

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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

Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome. / Peng, Suyuan; Fan, Yadan; Wang, Liwei; Wen, Andrew; Liu, Xusheng; Liu, Hongfang D; Shen, Feichen.

Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1302-1309 8621226 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

Peng, S, Fan, Y, Wang, L, Wen, A, Liu, X, Liu, HD & 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., 8621226, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1302-1309, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621226
Peng S, Fan Y, Wang L, Wen A, Liu X, Liu HD et al. Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1302-1309. 8621226. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621226
Peng, Suyuan ; Fan, Yadan ; Wang, Liwei ; Wen, Andrew ; Liu, Xusheng ; Liu, Hongfang D ; Shen, Feichen. / Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1302-1309 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).
@inproceedings{3eb42eca4e5d41ef8403bbd24f796b80,
title = "Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome",
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.",
keywords = "Association rule mining, CKD-MetS, Semantic MEDLINE Database",
author = "Suyuan Peng and Yadan Fan and Liwei Wang and Andrew Wen and Xusheng Liu and Liu, {Hongfang D} and Feichen Shen",
year = "2019",
month = "1",
day = "21",
doi = "10.1109/BIBM.2018.8621226",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1302--1309",
editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",
booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

}

TY - GEN

T1 - Leveraging Association Rule Mining to Detect Pathophysiological Mechanisms of Chronic Kidney Disease Complicated by Metabolic Syndrome

AU - Peng, Suyuan

AU - Fan, Yadan

AU - Wang, Liwei

AU - Wen, Andrew

AU - Liu, Xusheng

AU - Liu, Hongfang D

AU - Shen, Feichen

PY - 2019/1/21

Y1 - 2019/1/21

N2 - 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.

AB - 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.

KW - Association rule mining

KW - CKD-MetS

KW - Semantic MEDLINE Database

UR - http://www.scopus.com/inward/record.url?scp=85062483465&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062483465&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2018.8621226

DO - 10.1109/BIBM.2018.8621226

M3 - Conference contribution

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 1302

EP - 1309

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

ER -