臺灣博碩士論文加值系統

全民健保的財務規劃原本是以自給自足為原則，而在1995年全民健保實施以來，卻屢傳財務失衡的危機，隨著健保費用不斷的高漲下，為了避免醫療資源的浪費，有效的評估醫療資源分配成為一項重要的評估性指標。
肝癌是全世界五大常見的惡性腫瘤之ㄧ，台灣亦是肝癌高盛行區，近幾年的研究顯示，全球各地區肝癌發生率皆有上升的趨勢，其治療所花費的資源與費用比例是相當可觀的，因此，如何有效評估住院天數對於醫療資源分配與醫院管理是一個重要的議題且具挑戰性的工作。
本研究主要挖掘影響肝癌手術的相關因子來做探討，以健保資料庫中肝癌之患者為研究對象，運用人工智慧法中的粒子群最佳化演算法、倒傳遞類神經網路、支援向量機、分類迴歸樹、案例式推理等有效的評估肝癌手術患者的住院天數與醫療費用，以提供相關醫療人員參考依據。研究結果顯示，住院天數整體而言，除了單分類迴歸樹，其他準確度皆高於85%以上，其中以粒子群最佳化演算法結合倒傳遞類神經網路最高，為96.78%，在醫療費用準確度也達87.86%，結果顯示系統準確度之可行性，以提供醫師作為臨床診斷輔助參考，對於醫院醫療資源分配與病患家屬後續照顧上更能有效的控管。

The originally enactment planning for the national health insurance was based on the income-costs self-sufficiency principle. Since the promulgation of the national health insurance act in 1995, it has always fallen into the crisis and financial imbalance and deficits. Subsequently, the avoidance of medical resource wastes integrated with the effective estimation of the medical resource apportionment has thus become one of important evaluation indicators. Especially both the domestic and foreign researches all show that the length of stay for the surgery will directly influence the total medical costs, meanwhile.
The hepatocellular Carcinoma ranks the top 5th common malignant tumor globally amid Taiwan as a high-prevalent area unexceptionally. The researches in the past few years indicate that the global occurrence hepatocellular Carcinoma rates all remain in an ever-increasing tendency. The medical resources and medical treatment cost spent for such the hepatocellular Carcinoma are significantly tremendous. Such a situation, therefore, constitutes an important issue and a challengeable mission for how to evaluate effectively the length of stay for the surgery and medical resource appropriation and medical institution management.
With targeting on exploring all the relevant variables influencing the liver surgery, it has been adopted in this research some effective evaluation algorithm belonging to the Artificial Intelligence Method including the Particle Swarm Optimizer, Back-Propagation Neural Network, Support Vector Machine, Classification and Regression Tree, and Case-Based Reasoning for trying the overall average hepatocellular Carcinoma surgery patients’ length of stay (number of days), and medical treatment costs for providing relevant medical professionalism for evaluation basis and reference. The research results indicate that except the Classification and Regression Tree Algorithm, all other evaluation methods demonstrate precision rates more than 85% among which the portfolio of Classification and Regression Tree and Back-Propagation Neural Network achieves as high as 97.86%, ranking the top 1, and also a precision rate of 87.86% for analyzing medical treatment cost. Having reaching such so high feasibility, all those research results validate the adopted systematic precision rates can provided physicians auxiliary reference data for clinical diagnosis and assist the medical institutions in applying more effective control measures in medical resources apportionment and for help patients’ family conducting follow-up caring.

摘要 i
Abstract ii
誌謝 iv
目錄 v
表目錄 ix
圖目錄 xii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究重要性 3
1.3 研究目的 3
1.4研究範圍與限制 4
1.5論文架構 5
第二章 文獻探討 7
2.1肝癌簡介 7
2.1.1肝癌形成原因 7
2.1.2肝癌症狀 8
2.1.3肝癌臨床診斷 10
2.1.4肝癌治療方式 10
2.1.5肝癌相關文獻 11
2.2醫療資源耗用 12
2.2.1醫療資源耗用定義 12
2.2.2醫療資源耗用國內外相關研究 13
2.3資料探勘 14
2.3.1資料探勘簡介 14
2.3.2資料探勘流程 16
2.3.3資料探勘功能 17
2.3.4資料探勘應用於醫療領域 18
2.4 粒子群最佳演算法 19
2.4.1粒子群最佳演算法簡介 19
2.4.2粒子群最佳演算法速度更新法則 20
2.4.3粒子群最佳化演算法參數設定 23
2.4.4粒子群最佳化演算法相關研究 24
2.5類神經網路相關應用 25
2.5.1類神經網路簡介 25
2.5.2類神經網路發展史 25
2.5.3生物神經元模型 27
2.5.4類神經網路架構 28
2.5.5類神經網路架構分類 30
2.5.6倒傳遞類神經網路模式 32
2.5.7 倒傳遞類神經網路之參數 33
2.5.8類神經網路應用醫療領域 34
2.6支援向量機 35
2.6.1支援向量機要素 35
2.6.2線性支援向量機 36
2.6.3非線性支援向量機 36
2.6.4支援向量機應用於醫療領域 37
2.7決策樹演算法 37
2.7.1決策樹演算法簡介 37
2.7.2常用模型 39
2.7.3分類迴歸樹 40
2.7.4決策樹應用醫療領域 41
2.8案例式推理 41
2.8.1案例式推理運作流程 42
2.8.2案例式推理應用於醫療領域 44
2.9 ROC曲線 44
2.10交叉驗證 45
第三章 研究方法 47
3.1研究對象及資料來源 47
3.2研究架構 48
3.3研究變數 51
3.4研究變項定義 52
3.5 研究變項操作定義 55
3.6資料前置處理 56
3.7建構診斷模型 58
3.7.1建構粒子群最佳演算法診斷模型 58
3.7.2建構倒傳遞類神經網路模型 61
3.7.3建構分類迴歸樹模型 64
3.7.4建構支援向量機模型 65
3.8建構案例式推理系統模型 67
3.8模型評估 72
3.8.1混亂矩陣 72
3.8.2 ROC曲線 73
第四章 住院天數預測研究結果分析 74
4.1 研究資料敘述型統計 74
4.2粒子群最佳化演算法實驗分析 78
4.2.1粒子群最佳化演算法最佳參數組合 78
4.2.2粒子群最佳化演算法結果分析 79
4. 3倒傳遞類神經網路實驗分析 80
4.3.1倒傳遞類神經網路最佳參數組合 80
4.3.2倒傳遞類神經網路模型驗證分析 86
4.3.3倒傳遞類神經網路模型結果分析 86
4.3.4倒傳遞類神經網路績效評估 87
4. 4支援向量機實驗分析 88
4.4.1支援向量機最佳參數組合 88
4.4.2支援向量機驗證分析 90
4.4.3支援向量機績效評估 91
4.5粒子群最佳化演算法結合倒傳遞類神經網路實驗分析 92
4.5.1粒子群最佳化演算法結合倒傳遞類神經網路最佳參數組合 92
4.5.2粒子群最佳化演算法結合倒傳遞類神經網路模型驗證分析 95
4.5.3粒子群最佳化演算法結合倒傳遞類神經網路績效評估 95
4.6粒子群最佳化演算法結合支援向量機實驗分析 96
4.6.1粒子群最佳化演算法結合支援向量機最佳參數組合 97
4.6.2粒子群最佳化演算法結合支援向量機驗證分析 98
4.6.3粒子群最佳化演算法結合支援向量機績效評估 99
4.7倒傳遞類神經網路模型結合支援向量機實驗分析 100
4.7.1倒傳遞類神經網路模型結合支援向量機最佳參數組合 100
4.7.2倒傳遞類神經網路模型結合支援向量機驗證分析 102
4.7.3倒傳遞類神經網路結合支援向量機績效評估 103
4.8 分類迴歸樹實驗分析 104
4.8.1 分類迴歸樹模型分析 104
4.8.2 分類迴歸樹結果分析 107
4.8.3分類迴歸樹績效評估 108
4.9分類迴歸樹結合倒傳遞類神經網路實驗分析 109
4.9.1分類迴歸樹結合倒傳遞類神經網路最佳參數組合 109
4.9.2分類迴歸樹結合倒傳遞類神經網路模型驗證分析 112
4.9.3 分類迴歸樹結合倒傳遞類神經網路績效評 112
4.10分類迴歸樹結合支援向量機實驗分析 114
4.10.1分類迴歸樹結合支援向量機最佳參數組合 114
4.10.2分類迴歸樹結合支援向量機驗證分析 116
4.10.3 分類迴歸樹結合支援向量機績效評估 116
4.11案例式推理 117
4.11.1案例式推理系統介面 118
4.11.2案例式推理系統驗證分析 124
4.12住院天數預測結果 125
第五章 醫療費用預測研究結果分析 127
5.1案例式推理 127
5.2案例式推理系統介面 127
5.3案例式推理系統驗證分析 133
第六章 結論與建議 135
6.1結論 135
6.2建議 136
參考文獻 138
Extended Abstract 149
簡歷 155

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