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系統識別號 U0026-2308201913474600
論文名稱(中文) 應用大數據探討透析中低血壓與超過濾之研究
論文名稱(英文) An Initial Study of Using Big Data Analysis to Explore Intradialytic Hypotension and Ultrafiltration
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系
系所名稱(英) Department of Industrial and Information Management
學年度 107
學期 2
出版年 108
研究生(中文) 蔡東陞
研究生(英文) Dong-Sheng Cai
學號 R36061097
學位類別 碩士
語文別 中文
論文頁數 66頁
口試委員 指導教授-呂執中
口試委員-林耀欽
口試委員-謝佩璇
口試委員-陳平舜
中文關鍵字 大數據  末期腎病  血液透析  透析中低血壓  決策樹 
英文關鍵字 Big Data  Hemodialysis  Intradialytic Hypotension  Ultrafiltration  Decision tree 
學科別分類
中文摘要 若慢性腎病患者病狀進展至末期腎病階段時,需透過血液透析來治療末期腎病,而病患在透析過程中,最常見的風險為透析中低血壓。在進行血液透析期間,不同的臨床決策,將會進一步的影響血液透析中產生的風險,而應用大數據分析,將能改善臨床決策,由於臨床決策與醫療品質、病人安全之間有著極大的相關,因此有許多研究結果表示,透過大數據分析有助於醫療品質與病人安全的提升,且能協助新進醫護人員進行決策(Guha & Kumar, 2018; Prasad, Zakaria, & Altay, 2018; Saggi & Jain, 2018)。
透過大數據分析對於醫療產業將有相當大的幫助,而在醫療相關的數據研究中,較常使用的機器學習方法為決策樹,由於決策樹的每個決策階段都非常的明確,且執行效率也相當的高,此過程非常直覺單純又具有強大解釋力,因此本研究將使用決策樹來建立預測模型,以預測病患未來的血壓值,並得知關鍵變數如何影響病患之血壓,最後結合醫護人員專業的領域知識,探討應用大數據於醫療領域的可行性。
本研究使用台南個案醫院之末期腎病患者數據進行大數據分析,使用的資料集從2016年10月1日蒐集至2017年12月14日,共有131位末期腎病患者,經過數據預處理後得到43個變數,最終資料集共有936,700筆數據,將數據透過資料分區切割成訓練集與測試集,並使用決策樹來建立預測模型,最後得到的關鍵變數為:病患目前收縮壓、目標鈉離子濃度、機器設定溫度、人工腎臟編號、上次透析治療中發生低血壓之次數,並整理出病患在不同的收縮壓時,需針對不同的指標與規則來處理未來發生低血壓的風險;另外也針對性別來分別建立不同的決策樹預測模型,讓醫護人員能針對不同性別的病患,來進行不同的低血壓處理。根據研究結果,列出了幾點應用大數據在醫療領域的可行性,除了能提供預警指標之外,也能提供相關規則,以改善透析治療過程,進而減少資深醫師負擔並加速新進醫護人員的學習曲線。
英文摘要 When hemodialysis is used to treat end-stage kidney disease, a common risk is intradialytic hypotension. During hemodialysis, different clinical decisions will further affect the risks generated in hemodialysis. Applying big data analysis will improve clinical decision-making, because there is a great correlation between clinical decision-making and medical quality and patient safety. Therefore, many researches have shown that data analysis can improve medical quality and patient safety, and can assist new medical staff in making decisions.
In medical-related data research, the more commonly used machine learning method is the decision tree, because each decision stage of the decision tree is very clear and powerful. Therefore, this study will use decision trees to build predictive models. Finally, combined with the professional knowledge of medical staff to explore the feasibility of applying big data in the healthcare.
The data set was collected from October 1, 2016 to December 14, 2017 where there were 131 patients with end-stage kidney disease in the case hospital. There are 10 dependent variables that can be adjusted manually by the dialysis machine. The response variable is the "next systolic blood pressure" of the subjects. The final data set has 936,700 rows and 42 variables. The data set is further divided into training and test sets through data partition and predictive models are established by machine learning methods, namely decision tree. Finally, we obtained some research results through the decision tree model: the five key variables affecting systolic blood pressure in dialysis treatment. In addition, we also obtained five rules for patients with intradialytic hypotension risk in the future. Finally, based on the research results, the feasibility of applying big data in the medical field is listed. In addition to providing early warning indicators, it can also provide relevant rules to improve the dialysis treatment process. For junior staff, it can accelerate the learning curve, shorten the training schedule, reduce the burden of overall medical care, and then improve the quality of patient care and overall medical quality.
論文目次 摘要 I
英文延伸摘要 II
誌謝 VII
目錄 VIII
表目錄 X
圖目錄 XI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與限制 3
1.4 研究流程 3
第二章 文獻探討 5
2.1 腎臟疾病之現況 5
2.2 透析治療 7
2.2.1 透析中低血壓 9
2.2.2 超過濾 11
2.3 大數據與醫療產業相關之研究概況 13
2.4 文獻小節 21
第三章 研究方法 22
3.1 研究架構 22
3.2 數據蒐集 23
3.3 數據準備與預處理 23
3.4 數據說明 25
3.5 模型建立 25
第四章 實證研究與結果分析 28
4.1 實證研究背景 28
4.2 數據準備與預處理 31
4.3 數據說明 34
4.3.1 病人基本資料之變數 34
4.3.2 血液透析之自變數 34
4.3.3 透析機器可調整變數 37
4.3.4 血液透析之應變數 37
4.3.5 各變數說明 38
4.4 模型建立 42
4.4.1 以原始資料建立決策樹 42
4.4.2 以男性病患資料建立決策樹 47
4.4.3 以女性病患資料建立決策樹 49
4.5 研究結果小結 54
4.6 管理意涵 55
第五章 結論與建議 57
5.1 研究結論 57
5.2 未來研究建議 58
參考文獻 60
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