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研究生： 周曉林
Chow, Hiu-Lam
論文名稱： 失智症進展的心理社會決定因素：利用台灣老化長期研究的先進數據分析洞見
Psychosocial Determinants of Dementia Progression: Insights from Advanced Data Analytics using the Taiwan Longitudinal Study in Aging
指導教授： 簡士鎰
Chien, Shih-Yi
口試委員: 趙曉芳
Chao, Shiau-Fang
康藝晃
Kang, Yi-Huang
學位類別： 碩士
Master
系所名稱： 商學院 - 資訊管理學系
Department of Management Information System
論文出版年： 2024
畢業學年度： 113
語文別： 英文
論文頁數： 57
中文關鍵詞： 機器學習 、廣義線性混合效應模型樹 、因果樹 、因果森林 、失智症 、心理社會因素 、縱向研究
外文關鍵詞： Machine Learning, Generalized Linear Mixed-effects Model Tree, Causal Trees, Causal Forests, Dementia, Psychosocial Factors, Longitudinal Study
相關次數： 點閱：31  下載：9 
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    • 本研究探討失智症在老年人口中的進展情況，特別強調社會心理因素的影響。本研究利用中老年身心社會生活狀況長期追蹤調查（Taiwan Longitudinal Study in Aging, TLSA）數據，其中包含來自4869位個體在四個調查年度中的13088筆觀察數據，通過先進的數據分析技術來揭示社會心理因素與認知軌跡之間的複雜關係。本研究特別選擇使用具有可解釋性的樹狀模型，對於針對失智症進展的社會心理決定因素提供明確且可操作的見解。
    分析首先使用了廣義線性混合效應模型樹（Generalized Linear Mixed-Effects Model, GLMM Tree），以識別對認知結果隨時間變化有顯著影響的關鍵社會心理因素。該模型的解釋性提供了一個穩健的框架，用於理解這些因素在失智症進展中所扮演的動態角色。接著，通過使用因果樹（Causal Trees）和因果森林（Causal Forests）對數據進行分段，揭示異質性治療效果以及不同子群體中干預措施的差異性影響。這方法通過GLMM樹識別相關的社會心理因素，隨後使用因果樹和因果森林精細化目標干預措施，旨在提高模型的預測準確性，並為受失智症影響的個體提供更精確及具個性化的護理策略。

    This research investigates the progression of dementia within aging populations, with a particular emphasis on the influence of psychosocial factors. Utilizing data from the Taiwan Longitudinal Study in Aging, comprising 13088 observations from 4869 individuals over four survey years, advanced data analytics are applied to elucidate the intricate relationships between these factors and cognitive trajectories. The study employs tree-based models, specifically selected for their interpretability, which is critical for deriving clear and actionable insights into the psychosocial determinants of dementia progression.
    The analysis begins with the application of the Generalized Linear Mixed-Effects Model (GLMM) Tree to identify key psychosocial factors that significantly impact cognitive outcomes over time. The interpretability of this model provides a robust framework for understanding the dynamic role these factors play in the progression of dementia. Subsequently, Causal Trees and Causal Forests are employed to segment the data, revealing heterogeneous treatment effects and the differential impacts of interventions across distinct subgroups. This methodical approach—initially identifying pertinent psychosocial factors through the GLMM Tree, followed by the refinement of targeted interventions using Causal Trees and Causal Forests—aims to enhance the predictive accuracy of the models and generate more precise, personalized care strategies for individuals affected by dementia.

    摘要
    Abstract
    1. Introduction 1
    2. Literature Review 2
    2.1 Psychosocial Factors and Dementia 2
    2.2 Generalized Linear Mixed-effects Model Trees (GLMM Trees) 3
    2.3 Causal Trees and Causal Forests 4
    3. Methodology 5
    3.1 Variables 6
    3.2 GLMM Tree Analysis 8
    3.2.1 Data Pre-processing 8
    3.2.2 Model Building 10
    3.2.3 Model Performance Comparison 11
    3.2.4 Rule Interpretation of GLMM Trees 12
    3.3 Causal Tree and Causal Forest Analysis 16
    3.3.1 Data Pre-processing 17
    3.3.2 Causal Tree Model Building 24
    3.3.3 Linear Hypothesis Test 25
    3.3.4 Rule Interpretation of Causal Tree 27
    3.3.5 Causal Forest Model Building 34
    3.3.6 Estimation of Heterogeneous Treatment Effects 36
    4. Discussion and Conclusion 45
    References 47
    Appendix 51
    Appendix A. Table of Rules Extracted from GLMM Trees 51
    Appendix B. Table of Rules Extracted from Causal Trees 54

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