臺灣博碩士論文加值系統

中風後再入院增加了整體醫療花費支出。了解中風後再入院的原因，有利於照護資源分配及品質改善計劃。
本研究以台灣某大型區域醫院之中風登錄資料中剛住院的資料進行資料探勘，發展預測模型來預測急性中風住院病人的再入院或死亡，以建立其預測模型及探討其預測因子。
共收錄了4197位中風住院病人包含缺血性中風(n = 3165)，腦內出血(n = 445)及暫時性腦缺血發作(n = 587)。14天，30天，90天再入院或死亡的比率，分別為5.92%，9.61%，17.59%。在各時期再入院或死亡的預測模型中整體而言，C4.5或分類與迴歸樹的鑑別力最佳，而邏輯斯迴歸、隨機森林、支援向量機的鑑別力次之，多層感知器或k最近鄰居法的鑑別力最差。CART-風險樹顯示於各時期最高機率再入院或死亡的病人子群如下，14天為中風嚴重度執行命令困難併癌症；30天為中重度中風嚴重度併重度腎功能不佳且高尿酸血症；90天為鼻胃管灌食併輕中度腎功能不佳且肝功能不佳。影響各時期再入院或死亡的共同重要變數，包含過去一年曾住院次數、中風嚴重度、鼻胃管使用、風濕性心臟病、中風嚴重度執行命令。對於這些高風險族群的病人儘早進行出院計劃及照護轉銜介入可以減少再入院或死亡。

Readmission after stroke increases overall medical costs. The knowledge of the risk of readmission and its causes is essential for healthcare resource allocation and quality improvement planning.
By using data mining techniques on initial-admission data from the stroke registry of a large regional hospital in Taiwan, this study aimed to develop prediction models and to explore the predictive factors for readmission or mortality in patients hospitalized for stroke.
A total of 4197 stroke patients were hospitalized for ischemic stroke (n = 3165), intracerebral hemorrhagic (n = 445) and transient ischemic attack (n = 587). The 14-day, 30-day, 90-day rates of readmission or mortality were 5.92%, 9.61%, and 17.59% respectively. Among prediction models for readmission or mortality within each period, C4.5 and classification and regression tree (CART) methods had the highest discrimination, followed by logistic regression, random forest, and support vector machine techniques, whereas multilayer perceptron and k-nearest neighbor methods performed worst. Based on the CART classifier, patients with increased National Institutes of Health Stroke Scale (NIHSS) 1c (level of consciousness commands) score and cancer, those with moderate to severe NIHSS plus severe renal impairment and hyperuricemia, and those with nasogastric tube feeding plus mild to moderate renal impairment and hepatic function impairment carried the highest probability of readmission or mortality at 14 days, 30 days, and 90 days, respectively. Important predictive factors for readmission or mortality within the three periods included prior admission within 1 year, NIHSS score, nasogastric tube feeding, rheumatic heart disease, and NIHSS 1c score. Early hospital discharge program and transitional care intervention can be targeted for those patients with high risk of readmission or mortality.

第一章 緒論……………………………………………………………………….1
1.1. 研究背景……………………………………………………………………1
1.2. 研究動機……………………………………………………………………..3
1.3. 研究目的與問題 ……………………………………………………………5
第二章 文獻探討…………………………………………………………………6
2.1. 再入院 ………………………………………………………………………6
2.2. 中風後再入院………………………………………………………………..7
2.3. 再入院預測…………………………………………………………………16
2.4. 中風後再入院預測…………………………………………………………24
第三章 研究方法………………………………………………………………27
3.1. 研究架構…………………………………………………………………27
3.2. 研究變數………………………………………………………………30
3.2.1. 基本資料……………………………………………………35
3.2.2. 住院初始生命徵象以及檢驗數值…………………………36
3.2.3. 過去生活功能以及中風嚴重度…………………………39
3.2.4. 共病症或過去病史………………………………………43
3.3. 資料探勘技術………………………………………………………46
3.3.1. 決策樹…………………………………………………………46
3.3.2. 支援向量機……………………………………………48
3.3.3. k最近鄰居法………………………………………………48
3.3.4. 多層感知器 ……………………………………………49
3.3.5.邏輯斯迴歸…………………………………………………49
3.4. 實驗設計與評估方法………………………………………50
第四章 實驗結果與分析…………………………………………………53
4.1. 敘述性統計……………………………………………………………53
4.2. 各時期追蹤及再入院或死亡的比率……………………………..59
4.3. 各時期再入院或死亡的預測模型評估………………………………61
4.4. 自變數重要性排名…………………………………………………71
第五章 研究結論與建議……………………………………………………73
5.1. 研究結論…………………………………………………………73
5.2. 研究限制……………………………………………………….80
5.3. 研究建議..……………………………………………….….81
參考文獻…………………………………………………………………82
附錄 –IRB……………………………………………………………….94

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