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研究生:李宜澤
研究生(外文):Yi-Che Lee
論文名稱:應用以網格運算為基礎之免疫演算法改善高維度分數法於干擾控制之研究
論文名稱(外文):Use of High-Dimensional Propensity Score with Grid Computing based Immune Algorithm to Improve Confounding Control
指導教授:陳大正陳大正引用關係
指導教授(外文):Ta-Cheng Chen
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:51
中文關鍵詞:巨量健保資料庫比較效果研究高維度傾向分數傾向分數網格運算
外文關鍵詞:Big DataComparative Effectiveness ResearchHigh-Dimensional propensity scorepropensity scoreGrid computing
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  • 被引用被引用:1
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本研究利用台灣健保資料庫下,欲探討比較性的治療與效用(effectiveness)之研究時,如何於巨量資料(big data)中探勘出最具潛力的干擾共變數組合,藉此來修正干擾共變數所產生的偏差(Bias)而達到最佳的干擾調整。
近年來許多學者在藥物流行病學的研究中指出,應用以高維度傾向分數(hd-PS)為基礎的方法時,若能適當運用豐富資料量的巨量資料庫進行研究,所獲得的調整偏差來估算,則其結果與隨機試驗及觀察性研究會非常接近,能比其他方法獲得更佳的干擾調整。然而藉由該法如何由巨量資料中探勘出最具潛力的干擾共變數仍是過去文獻尚未能突破之處。因過去應用hd-PS於選擇潛在干擾共變數的過程中需高度仰賴由研究者或文獻所提供的專業知識及經驗。
透過本研究,提出一進化演算法,將hd-PS方法延伸轉換為一個於巨量健保資料庫中,進行重要干擾共變數的探勘,達成干擾調整的最佳化問題。因此本研究將提出一個以網格運算為基礎之進化演算法改善hd-PS方法來求取最佳干擾共變數之組合。研究結果顯示,本研究所提方法,所探勘出的潛在干擾共變數之組合,比過去hd-PS方法較易探勘出潛在的干擾共變數組合,治療與效用之研究達到勝算比最小化。

Through Taiwan’s Health Claim Database, this study investigated the confounding variable combination with the best potential in the big data based on comparative studies on therapy and effectiveness, thereby modifying bias arising form the confounding covariates and achieving the best confounding factor adjustment. In recent years, a number of scholars have pointed out in their research on drug epidemiology that when high-dimensional propensity score-based (hd-PS) approach is applied, through the proper use of rich data in the big database for research and the acquisition of adjusted bias for estimations, the results will be very close to stochastic testing and observational studies, thus deriving at better confounding factor adjustment compared to other methods.
However, how to explore confounding covariates with the highest potential from the big data remains to be a bottleneck faced in past literatures. In the past, the process of selecting confounding covariates using hd-PS involved high reliability on professional knowledge and experiences provided by researchers or literatures.
Through this study, an evolutionary computation approach was proposed to covert the hd-PS method in the Health Claim database with a massive amount of data for exploring important confounding covariates and achieving the optimization of confounding factor adjustment. Hence, a grid computing based evolutionary computation approach was proposed to improve the hd-PS method and obtain the best confounding covariate combination. Findings show that the method put forth in this study was better able to explore the potential confounding variable combination compared to past hd-PS methods, thus achieving the minimization of odds ratio in therapy and efficacy related studies.


摘要......i
Abstract......ii
誌謝......iii
目錄......iv
表目錄......vi
圖目錄......vii
符號說明......viii
第一章 緒論......1
1.1研究背景與動機......1
1.2研究目的......3
1.3研究步驟......4
第二章 文獻探討......6
2.1干擾共變數之探勘......6
2.2高維度傾向分數演算法流程......9
2.3勝算比......12
2.4免疫系統......13
2.4.1生物免疫系統介紹......13
2.4.2免疫演算法......14
2.5網格運算......17
第三章 研究方法......18
3.1問題描述......18
3.2問題模型定義...... 19
3.3以網格運算為基礎之進化算法......20
3.4系統架構......24
第四章 研究結果......26
4.1資料來源......27
4.2以hd-PS為基礎之試誤法......28
4.3以網格運算為基礎之進化算法之結果......29
4.4網格效能評測......33
第五章 結論......34
參考文獻......35
附錄一......40
簡歷(CV)......51


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