臺灣博碩士論文加值系統

根據衛生福利部國民健康署101年台灣地區因腦血管疾病死亡人數約30,800人，佔台灣十大死因第三位。腦血管病患中自發性腦出血死亡率最高，台灣自發性腦出血比例約20%~30%，遠高於西方國家的10%~15%。
部份自發性腦出血合併有腦室內出血及急性水腦症，需接受腦室外引流管置入(External Ventricular Drain:EVD)。某些病患拔除EVD後又因腦脊髓液阻塞而引發水腦症須裝置腦室腹腔引流(Ventriculo-Peritoneal Shunt:VPS)。根據資料，約20%的患者拔管後又需裝置VPS。
本研究採回顧病例方式進行研究，以中部某醫院之腦中風接受手術之病人為研究對象，使用電腦斷層掃描圖像區分腦出血或腦梗塞，判斷出血量以及出血位置，以統計方法進行資料分析病患拔管與否之關聯性，再以資料探勘找出影響因子，建構腦室外引流管的拔管預測模型。
本研究分析結果，顯示利用多層次類神經網路建置模型，準確率達92%，ROC曲線下面積達89%。後續在臨床上的使用，可以利用多層次類神經網路建立預測模型來判斷自發性腦出血病患後續是否會裝腦室腹腔引流管的可能性。

Cerebrovascular disease accounted for the third leading cause of death in Taiwan, according to the Ministry of Health and Welfare. It's cause approximately 30,800 people of deaths in Taiwan of 2012. Spontaneous intracerebral haemorrhage (sICH) is high incidence from 20% to 30 % in Taiwan.
In most circumstances, sICH patients with acute hydrocephalus is managed by insert of an external ventricular drain (EVD). However, after EVD removed, some patients with hydrocephalus caused by cerebrospinal fluid obstruction need to the insertion of a ventriculo-peritoneal shunt (VPS) as a permanent means of CSF drainage. Based on the hospital data after EVD removal cause Hydrocephalus is approximately 20%.
This study use case review of stroke patients undergoing surgery on a Regional Hospital in Middle Taiwan. The use of computer tomography images to distinguish cerebral hemorrhage or infarction, determine the amount of bleeding and hemorrhage location.This study use of statistical methods found that influence EVD removal factors and apply data mining to construct predictive model.
This study found that ANN outperform the other two methods with Correction rate is 92% and AUC 89%.It could use ANN established prediction model to determine whether sICH patients assessable will be placement VPS in future.

摘要 i
Abstract ii
誌 謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 3
1.4研究範圍與限制 3
1.5研究流程 3
第二章 文獻探討 5
2.1自發性腦出血 5
2.2腦室外引流管 7
2.3腦室腹腔引流管 9
2.4水腦症 10
2.5影像分割 11
2.5.1邊界導向 12
2.5.2區域導向 13
2.5.3影像分割應用於醫療相關研究 15
2.6羅吉斯迴歸 17
2.7資料探勘 17
2.7.1 決策樹 18
2.7.2 類神經網路 21
2.7.3資料探勘技術應用於醫學相關的研究 22
2.8相關研究匯整 24
第三章 研究方法 26
3.1研究架構 26
3.2資料收集及前置處理 28
3.2.1資料屬性 28
3.2.2 資料前處理 29
3.3影像切割 32
3.4模型建置 35
3.4.1決策樹 35
3.4.2 類神經網路 38
3.4.3羅吉斯迴歸 42
3.5模型績效評估 43
3.5.1K疊交叉驗證 44
3.5.3混淆矩陣 45
3.5.3 Calibration與Discrimination 46
3.5.4 ROC曲線 47
第四章 研究結果與分析 49
4.1基本資料 49
4.2信度分析 51
4.3 因子最佳切割點 52
4.4資料探勘建立預測模式與實證分析 60
4.4.1決策樹J48建立模型 60
4.4.2類神經網路建立模型 62
4.4.3羅吉斯迴歸建立模型 64
4.5模型績效評估 65
4.51 Calibration評估 65
4.5.2 Discrimination評估 66
第五章 結論與建議 67
5.1研究發現與討論 67
5.2結論 71
5.3未來研究建議 71
參考文獻 72

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