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研究生:陳榆臻
研究生(外文):Yu-Chen Chen
論文名稱:運用資料探勘中分類技術於預測住院老人跌倒之研究
論文名稱(外文):A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques
指導教授:詹前隆詹前隆引用關係
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:134
中文關鍵詞:高齡者跌倒分類決策樹資料探勘
外文關鍵詞:Geriatricsfallclassificationdecision treeData mining
相關次數:
  • 被引用被引用:5
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  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:1
減少病患因跌倒所造成傷害風險為2007年美國評鑑機構聯合會之子目標,預防住院老人跌倒為世界各國重大公共衛生議題之一。本研究整理過去文獻之跌倒風險因子,並收集國內北部一間教學型醫院之602筆跌倒與非跌倒基本資料(年齡、入院方式等);入院時的生理狀況變項(疾病診斷、肌力等);入院、住院當中跌倒危險因子審核記錄;日常生活能力(巴氏量表)。其研究目的為運用資料探勘中的分類技術,建構一個住院跌倒之風險預測模式,找出影響有無跌倒之重要預測因子;除此之外,依不同科別檢視影響該科住院別病患之重要危險因子;以提供未來醫院在審視病患時,能在病患入院之初更有效的篩選最需醫療照護之病患;又病患從入院到出院這段過程中,生理狀況會因治療或其他因素而有所不同,因此本研究針對跌倒危險因子審核記錄,選取兩個時間點做探討,一為入院時,二為住院當中。研究結果發現,針對全部科別,過去一年是否有跌倒歷史為影響有無跌倒最重要之變數;但對外科而言,跌倒(出院)前是否步態不穩或需使用輔具以及入院時是否有意識欠清、無定向感、躁動不安是重要之預測因子;另外,外科病患若入院方式為急診也和跌倒有顯著相關。運動反應是本研究發現的新議題,運動反應評估為6分(能服從指令動作)以下,則此病患有高度跌倒風險,最後,本研究利用四種分類方法做分析,其平均訓練資料準確度都能從五成達到七成以上,而測試資料也達到七成以上,因此達到準確度之外,一般性也佳。
Reduce the risk of patient harm resulting from falls is a sub goal of the Joint Commission on Accreditation of Healthcare Organizations of 2007. So prevent the elderly from falling is one of the important public health issue of countries all over the world. The sub-project coordinated fall risk factors of past literature and collected 602 fall and non-fall admission data includes age, admission type, diagnosis, muscle power, admission and fall(discharge) fall risk assessment, Barthel indexs from researching hospital of north Taiwan. The research proposed to use data mining to construct inpatient fall risk forecast model and find significant patterns and prediction factors of fall and non-fall. In addition, the research according to different surgery first, and then look over that influences its surgery''s important dangerous factor. It can provide hospital to examine patients more efficiency. Finally, the accuracy rate of training data were up to 70% ,and validation date were also achieved 70%. Therefore, the model can achieve not just accuracy, but general.
書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 iv
誌謝 vi
目錄 viii
表目錄 xi
圖目錄 xii
1 緒論 1
1.1 究背景與動機 1
1.2 研究目的與重要性 2
1.3 研究流程 2
2 文獻探討 4
2.1 跌倒的風險因子 4
2.1.1 外科(Surgery) 與跌倒 4
2.1.2 病患內在健康因素 4
2.1.3 心理狀態(Mental state)與跌倒 4
2.1.4 移動性(Mobility)與跌倒 4
2.1.5 跌倒歷史(History of falls)與跌倒 5
2.1.6 如廁需求(Toileting needs)與跌倒 5
2.1.7 年齡(Age)與跌倒 5
2.1.8 性別(Gender) 與跌倒 6
2.1.9 住院天數(Length of stay) 與跌倒 6
2.1.10 巴氏量表(Barthel index) 與跌倒 6
2.1.11 睡眠問題(Sleep problem)與跌倒 6
2.1.12 充血性心臟衰竭(congestive heart failure) 與跌倒 7
2.1.13 暈眩(Dizziness)與跌倒 7
2.1.14 視力(Vision )與跌倒 7
2.1.15 酗酒習慣(Intoxication)與跌倒 7
2.1.16 其他因子(Miscellaneous factors)與跌倒 7
2.1.17 風險因子總結 7
2.2 統計方法 9
2.3 獨立T檢定 9
2.4 卡方檢定 9
2.5 邏輯斯迴歸 9
2.6 資料探勘 10
2.6.1 資料探勘定義 10
2.7 決策樹 10
2.7.1 決策樹分類法簡介 10
2.7.2 決策樹的產生程序 10
2.7.3 基本演算法 11
2.7.4 決策樹-C5.0 11
2.7.5 決策樹-CART 12
2.7.6 決策樹-CHAID 13
2.7.7 決策樹之優缺點 13
2.7.8 決策樹在醫療領域的應用 14
3 研究方法 15
3.1 研究設計 15
3.2 研究架構 15
3.3 研究變項操作型定義 17
3.4 資料分析方法 19
3.5 模式驗證 20
3.6 模型評估 21
4 研究結果 22
4.1 描述性統計分析 22
4.1.1 跌倒病人住院科別 22
4.1.2 病人跌倒發生情況 22
4.1.3 跌倒原因及傷害程度 22
4.2 獨立樣本T檢定 23
4.2.1 全部 23
4.2.2 內科 23
4.2.3 精神科 23
4.2.4 外科 24
4.3 卡方檢定分析 24
4.3.1 全部 24
4.3.2 內科 28
4.3.3 精神科 32
4.3.4 外科 34
4.4 邏輯斯迴歸分析 36
4.4.1 全部 36
4.4.2 內科 37
4.4.3 精神科 38
4.4.4 外科 39
4.5 決策樹 40
4.6 C5.0 40
4.6.1 全部 40
4.6.2 內科 41
4.6.3 精神科 41
4.6.4 外科 42
4.7 CART 42
4.7.1 全部 42
4.7.2 內科 43
4.7.3 精神科 43
4.7.4 外科 44
4.8 CHAID 45
4.8.1 全部 45
4.8.2 內科 45
4.8.3 精神科 46
4.8.4 外科 47
4.9 總結 47
4.9.1 各方法屬性之選擇 47
4.9.2 各方法屬性之排序 48
5 結論與建議 50
5.1 研究發現及討論 50
5.2 研究貢獻 51
5.3 研究限制 52
5.4 研究建議及未來研究方向 52
參考文獻 53
附錄 57
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