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系統識別號 U0026-0802201816234300
論文名稱(中文) 建置醫療資料探勘架構:以血液透析通路治療為例
論文名稱(英文) Design of a Clinical Decision Support System – Case of a Hemodialysis Unit
校院名稱 成功大學
系所名稱(中) 高階管理碩士在職專班(EMBA)
系所名稱(英) Executive Master of Business Administration (EMBA)
學年度 106
學期 1
出版年 107
研究生(中文) 林軒名
研究生(英文) Hsuan-Ming Lin
電子信箱 vierylin@gmail.com
學號 R07044864
學位類別 碩士
語文別 中文
論文頁數 59頁
口試委員 指導教授-呂執中
口試委員-林啟禎
口試委員-謝佩璇
口試委員-林耀欽
中文關鍵字 血液透析  資料探勘  透析低血壓  臨床決策支援 
英文關鍵字 Data Mining  Clinical Decision Support  Hemodialysis  Intradialytic hypotension 
學科別分類
中文摘要 隨著資訊技術硬體快速發展,許多產業開始應用大數據進行管理系統和流程之分析與改善。醫療與生技產業不僅複雜且與生命息息相關,透過大數據與資料探勘的協助,提高臨床診斷與治療的準確率與品質,將為人類帶來巨大福祉。末期腎病病患由於常合併多種內科疾病,例如糖尿病、高血壓、心血管疾病或腦中風,當接受血液透析,由於部分血液在體外循環,且在短時間內需要移除水分,常導致透析中血壓高低變化,甚至引發休克。透析中發生低血壓往往與增加病人整體死亡風險、降低透析品質,與病人產生諸多症狀相關。
本研究根據資料探勘流程,並結合醫療分析流程,從提出血液透析低血壓之問題開始,分析個案醫院之腎臟科臨床資訊與臨床治療流程,利用該醫院之線上資料庫,建立整合性資料庫與醫療資料倉儲架構。再經過資料預處理,將資料進行分析以建立模型,得到此模型在測試資料中,準確度為80%而敏感度為49.78%。模擬導入臨床單位後,使用實際案例讓臨床護理人員進行偕同評估,得到準確度82.5%, 精確度69.1%以及敏感度 65.31%。此外經護理長與主治醫師進行評估,認為本系統可以協助臨床人員提高評估頻次,使第一線同仁可以即早針對預期會發生低血壓之病人進行處理。於管理方面,也能有效減少護理工作壓力,並提高醫療照護品質。
最後將上述研究流程,彙整建立一個醫療資料探勘與決策輔助系統架構,並建立血液透析低血壓之預警系統。 透過本架構,提供研究者後續進行醫療資料探勘與決策支援系統之研究架構,也提供管理者建立臨床決策支援系統之流程,以其提高醫療照護品質並減少醫療成本。
英文摘要 To improve the quality of medical decision is critical for current health care system. The technique of big data and data mining provide the potential to realize the goal. This study aims to establish a framework of data mining and decision support system in health care industry. We applied data mining process in the hemodialysis unit of a case hospital retrospectively from Oct. 2016 to Dec. 2017. A total of 21,974 sessions with 3,438,585 machine data were included. After data pre-preparation, the final data set of machine data totals 1,095,198. The target variable was defined by low systolic pressure (<90mmhg) or intradialytic hypotension (20mmhg changed). We split dataset into training and test dataset. The model was trained with Tree model (CART) and training dataset. The accuracy and recall of training model dataset were 80.3%/51.7%. The model was tested with test model and it reported 80.3% accuracy / 49.78% recall. Deployment of the decision support system in the case hemodialysis unit was further simulated to verify its validity. Three hemodialysis staff made prediction according to the data as well as the results previously predicted by the model. The prediction had 82.5% accuracy, 69.1% precision and 65.31% recall .
Finally, a medical data mining and decision support system framework was established accordingly. An early warning system of intradialytic hypotension was established. The proposed framework provides researchers the process for data mining and decision support systems in this specific industry. It also establishes the structure of a clinical decision support system to improve the quality of healthcare and to reduce the cost in the future.
論文目次 中文摘要 I
EXTENDED ABSTRACT II
表目錄 X
圖目錄 XI
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 3
第二章 文獻探討 5
第一節 資料科學概況 5
第二節 大數據 6
第三節 資料探勘與機器學習 8
第四節 資料探勘流程之建構 13
第五節 醫療產業應用資料探勘與機器學習之現況 15
第六節 慢性腎病與透析 19
第七節 文獻小結 25
第三章 研究方法 26
第一節 定義問題與資料 27
第二節 建立資料倉儲 28
第三節 資料收集與處理 28
第四節 模型建構與測試 30
第五節 結果評估與應用 32
第四章 實證與結果 33
第一節 問題定義與個案介紹 33
第二節 資料倉儲建立 37
第三節 資料預處理 37
第四節 模型建構 44
第五節 解釋與評估 49
第六節 醫療資料探勘與決策支援架構 53
第五章 結論與建議 54
第一節 結論與研究貢獻 54
第二節 研究限制與建議 55

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