臺灣博碩士論文加值系統

本論文係針對醫療器材PCA(Patient Controlled Analgesia, 病患自控式止痛)開發一資料蒐集系統(Data Acquisition System)，並建立癌症末期病患之PCA藥量使用模型以進行用藥量預測與PCA藥量使用類型分析。
本文根據34位(口腔癌、肺癌、肝癌等9種)癌症末期病人使用PCA之每日藥量記錄，經由AR(Auto-regression)模型分析後，依據癌末病人PCA用藥量之時間序列其增減之快慢及振盪程度等特性而區分為數種主要PCA藥量使用類型，並可評估AR模型之準確度。除此之外，尚可依據AR模型之特徵參數進行短期之用藥量預測。吾人依據個別病患20天之藥量記錄來計算其時間序列AR模型之特徵參數，再以此參數分別預測1天後、3天後及10天後之用藥量，觀察其誤差低於30%的成功率。並考慮PCA藥量使用類型之變化，此一現象可能係因病況的改變或因其他的治療所導致，觀察此一現象是否對用藥量之預測造成影響。
研究結果發現：當病人之PCA藥量使用類型穩定時， 使用AR模型預測1天後、3天後及10天後之用藥量，誤差低於30%之成功率分別為82.41%、70.29%、47.36%，而病人之類型有明顯變動時，成功率則明顯降低，因此若病人之類型無明顯之變化時，吾人所提出之方法將可有效進行預測。

In this thesis, we developed a Data-Acquisition (DAQ) System for Patient-controlled analgesia (PCA). The advantages of the DAQ are portable, convenient, efficient and useful for user. Then we analyze the data which collected by the DAQ, we attempt to model the PCA time history with simple AR(Auto-regressive) model and classify the patients'' data for several parts.
According to 34 cancer patients'' data, we can find that the scale of oscillation and the speed of increase or decrease for PCA dose with each patient''s PCA time history were different. Then we can use the information of PCA time history for classifying the patients with several parts. And we modeled the data with AR model for each patient, we predicted the patients'' PCA dose for future with the AR model. Our rates of success for every 1 day, 3 days and 10 days were 82.41%, 70.29% and 47.36%.
In this thesis, our method was offering an information of different focus for doctor to compare with their knowledge and make a better clinical reserch.

書名頁……………………………………………………………i
論文指導教授推薦函…………………………………………...ii
論文口試委員審定書……………………………………………..iii
授權書……………………………………………………………..iv
中文摘要………………………………………………………….vii
英文摘要……………………………………………………………viii
誌謝辭……………………………………………………………..ix
目錄……………………………………………………………..x
表目錄……………………………………………………………..xiii
圖目錄……………………………………………………………..xiv
一、 緒論………………………………………………………..1
1.1 PCA之簡介與研究背景……………………………………..1
1.2 研究動機……………………………………………………..3
1.3 研究目的……………………………………………………..5
1.4 本文架構……………………………………………………..6
二、 文獻回顧…………………………………………………..7
2.1 PCA應用於手術後之研究…………………………………..7
2.2 ARMA模型……………………………………………………..8
2.2.1 ARMA模型之簡介…………………………………………..8
2.2.2 線性預測…………………………………………………..10
三、 PCA資料蒐集系統…………………………………………..12
3.1 資料蒐集系統之組成元件…………………………………..13
3.1.1 P to S訊號轉換發射器…………………………………..13
3.1.2 Data Box資料蒐集器……………………………………..17
3.2 資料蒐集系統功能介紹……………………………………..19
3.2.1 PCA傳輸至資料收集器…………………………………..19
3.2.2 資料收集器傳輸至PC……………………………………..20
3.3 資料蒐集系統操作方式……………………………………..23
3.3.1 使用者操作方法…………………………………………..23
3.3.2 系統運作方式…………………………………………….. 24
四、 PCA藥量的數學模型與藥量預測…………………………..32
4.1 AR模型之應用………………………………………………..34
4.1.1 選擇適當的AR模型………………………………………..34
4.1.2 AR模型參數之計算………………………………………..39
4.1.3 整體系統方塊圖…………………………………………..42
4.2 預估效果之評估……………………………………………..44
4.2.1 成功率……………………………………………………..44
4.2.2 誤差之均方根值…………………………………………..48
4.3 AR模型之驗證………………………………………………..49
4.3.1 誤差分佈圖………………………………………………..49
4.3.2 自我相關函數圖…………………………………………..50
4.4 短期預測與長期預測之比較………………………………..50
4.5 結果與討論…………………………………………………..53
五、 PCA藥量的數學模型與特性分類…………………………..54
5.1 PCA藥量使用類型簡介……………………………………..54
5.1.1 用藥模式分析之步驟……………………………………..54
5.1.2 用藥類型之分類…………………………………………..55
5.1.3 各模式之典型時間序列…………………………………..57
5.2 數學模型的準確性與分類之成效…………………………..58
5.2.1 數學模型的準確性………………………………………..58
5.2.2 分類之成效………………………………………………..63
5.3 PCA藥量使用類型之穩定性………………………………..64
5.4 結果與討論…………………………………………………..70
六、 成果討論及未來發展……………………………………..72
6.1 成果討論……………………………………………………..72
6.2 本文貢獻……………………………………………………..73
6.3 未來發展方向………………………………………………..74
參考文獻…………………………………………………………..75
附錄……………………………………………………………..78
個人簡歷…………………………………………………………..85

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