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
AU - Shen, Feichen
N1 - Funding Information:
VI. CONCLUSION Through the use of association rule algorithms, associations between pathophysiological mechanisms common to both CKD and MetS were identified. It is likely that these pathophysiological mechanisms that effect CKD-MetS also plays an important role in further progression of kidney damage. Through this study, we can provide clinicians with the focus and key points of management for CKD-MetS. In the future, we will include phenotypic and genotypic characterizations to conduct a thorough analysis for CKD-MetS.[35-39] ACKNOWLEDGMENT This study was supported by Traditional Chinese Medicine Bureau of Guangdong Province, China (Chinese medicine preparation R&D project: Sanqi oral preparation): Xusheng Liu (2015KT1535); Guangzhou Science and Technology Program key projects: Yi-Fan Wu (2016201604030022); Guangdong Science and Technology Department: Yi-Fan Wu (2014A020221087) and Guangdong Provincial Hospital of Chinese Medicine Program: Yi-Fan Wu (2017KT1642).
Publisher Copyright:
© 2018 IEEE.
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
AN - SCOPUS:85062483465
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.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -