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研究生: 杜宜庭
Tu, I-Ting
論文名稱: 病人再入院之深度學習預測模型
Patient-Readmission Forecasting using Deep Learning
指導教授: 李昇暾
Li, Sheng-Tun
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 62
中文關鍵詞: 再入院強化學習深度Q學習網路時間序列早期預測文字探勘
外文關鍵詞: readmission, reinforcement learning, DQN, early prediction on time series, text mining
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  • 摘要 I 英文延伸摘要 II 致謝 VIII 目 錄 X 表 目 錄 XIII 圖 目 錄 XIV 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 研究範圍 3 1.4 研究流程 3 第二章 文獻探討 5 2.1 再入院率議題與研究 5 2.1.1 再入院議題 5 2.1.2 再入院相關研究 6 2.2 多變量早期時間序列預測 8 2.3 主題模型 9 2.3.1 潛在語義分析和概率潛在語義分析 9 2.3.2 隱含狄利克雷分佈 10 2.4深度學習 12 2.4.1 卷積神經網路 12 2.4.2 長短期記憶 15 2.5強化學習 17 2.5.1 強化學習基本架構 18 2.5.2 Q-Learning 19 2.5.3 深度Q學習網路 20 2.5.4 Double DQN和 Dueling DQN 22 第三章 研究方法 24 3.1 問題及符號定義 24 3.1.1問題定義 24 3.1.2符號定義 25 3.2 研究框架 26 3.3 資料前處理 27 3.4 特徵萃取 29 3.5 強化學習框架 31 3.5.1 狀態、動作及環境設定 31 3.5.2 獎懲值設定 32 3.5.3 代理人設定 32 3.5.4 建置流程 33 第四章 實驗結果與分析 35 4.1 資料集說明 35 4.2 超參數設定 37 4.3 評估指標 40 4.3.1 早期預測評估指標 40 4.3.2 模型準確率評估指標 40 4.4 實驗評估 42 4.4.1 字典比較 42 4.4.2 演算法比較 45 4.4.3 資料集調整 50 4.5 病人再入院比例評估 51 第五章 結論與未來展望 54 5.1 結論與貢獻 54 5.2 未來展望與研究限制 55 參考文獻 57

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