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

机译:利用关联规则挖掘发现慢性肾脏病并发代谢综合征的病理生理机制

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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.
机译:这项研究的目的是探讨患有慢性肾脏疾病并代谢综合征(CKD-MetS)的患者的病理生理机制,以便更好地支持积极治疗。应用关联规则挖掘从语义MEDLINE数据库(SemMedDB)中提取重要的关联。我们的数据集中总共包含23,310个PMID和5,542个唯一项。我们重点研究了提取的5种特定综合症:糖尿病,心血管疾病,甘油三酸酯增加,肥胖症和炎症。针对这五种疾病生成的规则数分别为SO,197、31、21和21。我们的研究确定了CKD-MetS患者中存在的几种病理生理机制,这些机制可能导致进一步的肾脏损害。

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