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研究生(外文):LO, CHIAO-YU
論文名稱(外文):Constructing Predictive Model to Predict important Factors for In-Hospital Cardiac Arrest patients
指導教授(外文):CHENG, BOR-WEN
外文關鍵詞:In-Hospital Cardiac Arrest (IHCA)Clinical Alert System (CAS)Decision tree C5.0Support Vector Machine(SVM)
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住院病患安全是醫院醫療品質的重要指標,而影響的主要原因是院內心跳停止事件,該事件發生頻率極高,但是經過急救後存活下來的機率是極低的。儘管,採用臨床警示系統(clinical alert system , CAS)取代人為監控,提高通報住院病患的生命徵象異常事件之次數,死亡率卻沒有降低。因此本研究想提升醫院CAS之敏感度與特異性,預先讓醫護人員發現狀況不穩定之病患,提前給予適合的醫療處理,有效的降低院內不預期心跳停止事件的發生及死亡率。

The safety of inpatients is an important indicator for hospital healthcare quality, so the in-hospital cardiac arrest(IHCA) has always been the main reason. Because the frequency of occurrence is extremely high, and the survival rate is extremely low after first aid. Although, hospital use clinical alert system(CAS) substitute human monitor in order to increase the number of alarm, but the mortality has not decreased. Thus, in this study will promote sensitivity and specificity for CAS. The medical personnel will find patients with unstable conditions in advance after CAS is improved, then give inpatients appropriately medical treatment to effectively reduce the events occurrence and mortality.
Therefore, in this study will collect the data of the clinically abnormal events with the CAS standards and the adult inpatients who over twenty years old from in case hospital database during 2016 to 2018.Using decision tree C5.0 and Logistic regression to find the correlation between independent variables and dependent variables, and then we use the decision tree C5.0 , Logistic regression and support vector machine(SVM) to conduct data mining and analysis to find out the impact. The important factors of in-hospital cardiac arrest events and building a predictive models. Finally, we use the accuracy and ROC curve as the model performance evaluation criteria. Based on this result , the best predicted model is the decision tree C5.0 , the accuracy is 83.93%. This result can be as a reference for physician in clinical medicine.

摘要 i
Abstract ii
目錄 iii
表目錄 vi
圖目錄 vii
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研究對象、範圍與限制 3
1.4名詞定義 3
1.5研究流程 4
第二章 文獻探討 6
2.1院內心跳停止事件 6
2.2臨床警示系統 6
2.2.1院內CAS啟動內容與流程 8
2.3快速反應小組 11
2.3.1 院內加入RRT小組工作 12
2.4決策樹 13
2.5支援向量機(Support Vector Machine , SVM) 15
2.5.1線性SVM基本概念 16
2.5.2 SVM核心函數 17
2.6羅吉斯迴歸運用在醫療上 19
2.7本章小節 21
第三章 研究方法 22
3.1研究架構 22
3.2研究資料 24
3.2.1資料來源 24
3.2.2資料蒐集 24
3.3資料前處理 27
3.4支援向量機SVM 28
3.4.1支援向量機SVM模型 28
3.4.2支援向量機SVM參數設定 29
3.5預測模型建構 29
3.5.1 k-fold交叉驗證法(k-fold cross-validation) 29
3.5.2 建構決策樹C5.0模型 30
3.6模型績效評估 33
第四章 研究結果 35
4.1決策樹C5.0模型 35
4.1.1決策樹C5.0模型訓練樣本組及測試樣本組選擇 35
4.1.2決策樹C5.0模型結果 36
4.2羅吉斯迴歸模型 37
4.2.1羅吉斯迴歸模型訓練樣本組與測試樣本組選擇 37
4.2.2羅吉斯迴歸模型結果 37
4.3支援向量機SVM模型 38
4.3.1支援向量機模型最佳參數組合 39
4.3.2支援向量機SVM模型一訓練樣本組與測試樣本組選擇 39
4.3.3支援向量機SVM模型一結果 40
4.3.4支援向量機SVM模型二訓練樣本組與測試樣本組選擇 41
4.3.5支援向量機SVM模型二結果 41
4.3.6支援向量機SVM模型三訓練樣本組與測試樣本組選擇 42
4.3.7支援向量機SVM模型三結果 43
4.4模型績效評估 44
第五章 結論與建議 49
5.1研究發現與結論 49
5.2未來方向研究與討論 50
參考文獻 52

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