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臺灣博碩士論文加值系統

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研究生:陳冠宇
研究生(外文):Guan-yu Chen
論文名稱:右設限資料下深度學習分析
論文名稱(外文):Deep Learning Analysis Under Right Censored Data
指導教授:謝進見
指導教授(外文):Jin-Jian Hsieh
口試委員:邱海唐潘宏裕
口試委員(外文):Hai-Tang ChiouHung-Yu Pan
口試日期:2024-06-24
學位類別:碩士
校院名稱:國立中正大學
系所名稱:數學系統計科學研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:37
中文關鍵詞:深度學習廣義加法性模型類神經網路部分排序估計右設限資料存活分析
外文關鍵詞:deep learninggeneralised additive modelneural networkpartial rank estimationright-censored datasurvival analyses
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  • 被引用被引用:0
  • 點閱點閱:17
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這篇論文主要探討在右設限資料下使用深度學習的方式預測存活時間,目前的存活分析大都是以預測風險函數和生存函數為主,很少直接預測存活時間。本論文藉由PRE估計模型參數,並使用GAM做擬和。為了讓預測更準確,我們使用一些選點的規則,最後將資料餵進去NN,得到最後的預測模型。透過模擬實驗,和現有的用於生存分析的深度神經網絡模型做比較,最後應用此方法分析三筆真實資料。
This paper primarily discusses using deep learning methods to predict survival time in the context of right-censored data. Currently, most survival analysis focuses on predicting risk functions and survival functions, with few directly predicting survival time. This paper estimates model parameters using the PRE approach and fits the model using Generalized Additive Models (GAM). To enhance prediction accuracy, we employ certain point selection rules, ultimately feeding the data into a neural network to obtain the final prediction model. Through simulation experiments, we compare this method with existing deep neural network models for survival analysis, and finally apply this method to analyze three real datasets.
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . .1
2 Data and model . . . . . . . . . . . . . . . . . . . . . . . . .3
2.1 Right censored data . . . . . . . . . . . . . . . . . . . . . 3
2.2 GAFT model . . . . . . . . . . . . . . . . . . . . . . . . .. 4
2.3 Models of survival analysis . . . . . . . . . . . . . . . . . 5
2.3.1 Accelerated Failure Time Model . . . . . . . . . . . . . . .5
2.3.2 Cox Proportional Hazards Model . . . . . . . . . . . . . .. 5
3 Literature review. . . . . . . . . . . . . . . . . . . . . . . .7
3.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Deepsurv . . . . . . . . . . . . . . . . . . . . . . . . . . .10
3.3 Deephit . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Buckley-James Estimator . . . . . . . . . . . . . . . . . . . 15
4 Estimation procedure . . . . . . . . . . . . . . . . . . . . . .17
4.1 Partial rank estimator . . . . . . . . . . . . . . . . . . . .17
4.2 Generalized Additive Models . . . . . . . . . . . . . . . . . 18
4.3 Estimation procedure . . . . . . . . . . . . . . . . . . . . .20
5 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . .23
5.1 Model settings . . . . . . . . . . . . . . . . . . . . . . . .23
5.2 Performance of the model with n=400 . . . . . . . . . . . . . 27
5.3 Performance of the model with n=800 . . . . . . . . . . . . . 29
6 Real data analysis . . . . . . . . . . . . . . . . . . . . . . .32
7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . .35
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
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