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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 杜裕康 | zh_TW |
| dc.contributor.advisor | Yu-Kang Tu | en |
| dc.contributor.author | 林芷瑋 | zh_TW |
| dc.contributor.author | Chih-Wei Lin | en |
| dc.date.accessioned | 2026-08-28T16:32:36Z | - |
| dc.date.available | 2026-08-29 | - |
| dc.date.copyright | 2026-08-28 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-07-31 00:00:00 | - |
| dc.identifier.citation | 1 Tan, S., Zhou, J., Veang, T., Lin, Q. & Liu, Q. Global, regional, and national burden of atrial fibrillation and atrial flutter from 1990 to 2021: sex differences and global burden projections to 2046-a systematic analysis of the Global Burden of Disease Study 2021. Europace 27 (2025). https://doi.org/10.1093/europace/euaf027
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/104587 | - |
| dc.description.abstract | 對於新診斷心房顫動（atrial fibrillation, AF）患者，如何準確預測中風風險並選擇最適治療策略，仍為臨床與研究上的重要挑戰。特別是在AF診斷初期之動態臨床管理階段，治療決策具有高度不確定性，且同時存在死亡競爭風險，使風險評估更為複雜。本研究旨在建立具可解釋性之機器學習模型以進行短期中風風險預測，並結合競爭風險分析架構，評估早期抗凝血治療策略之臨床效果。
本研究使用來自多中心醫療體系之大型電子病歷資料庫，納入人口學特徵、共病狀況與用藥資訊等變數，建構邏輯斯迴歸（logistic regression, LR）與極端梯度提升（extreme gradient boosting, XGBoost）預測模型。模型效能透過內部測試及兩個獨立世代之外部驗證進行評估，並以受試者操作特徵曲線下面積（area under the receiver operating characteristic curve, AUROC）、校準指標、決策曲線分析（decision curve analysis, DCA）及淨重新分類改善指標（net reclassification improvement, NRI）等方法，全面評估模型之鑑別能力、校準度與臨床應用價值。在治療策略評估方面，針對不同早期抗凝血策略（包括直接口服抗凝血藥物、Warfarin及未使用抗凝血治療）之臨床效果比較，本研究採用時間－事件（time-to-event）分析方法，結合landmark時間點設計，並考量死亡為競爭事件，建立Fine–Gray次分布風險模型進行分析。 研究結果顯示，所建立之機器學習模型在1年中風風險預測上顯著優於傳統CHA₂DS₂-VASc評分系統，且透過特徵貢獻分析方法維持模型之可解釋性。競爭風險分析結果則顯示，在調整相關共變數後，各治療策略於中風及重大出血風險上並無顯著差異。 綜上所述，本研究建立之整合性分析框架，結合可解釋人工智慧與嚴謹之流行病學方法，展現出提升個別化風險預測與輔助臨床決策之潛力，並為心房顫動患者之精準醫療提供重要實證基礎。 | zh_TW |
| dc.description.abstract | Accurate stroke risk prediction and optimal treatment strategy selection remain critical challenges in patients with newly diagnosed atrial fibrillation (AF), particularly during the early post-diagnosis period characterized by dynamic clinical management and competing mortality risks. This study aims to develop and validate interpretable machine learning models for short-term stroke prediction, and to evaluate early anticoagulation strategies using a competing risk framework.
Using a large-scale electronic health record dataset from a multi-center healthcare system, we constructed prediction models based on logistic regression (LR) and extreme gradient boosting (XGBoost), incorporating demographic characteristics, comorbidities, and medication use. Model performance was assessed through internal testing and external validation across independent cohorts. Discrimination, calibration, and clinical utility were evaluated using area under the receiver operating characteristic curve (AUROC), calibration metrics, decision curve analysis, and net reclassification improvement. To address time-to-event outcomes with competing risks, Fine–Gray sub-distribution hazard models were applied to compare early anticoagulation strategies, including direct oral anticoagulants, warfarin, and no anticoagulation, using a landmark design. The results demonstrated that the machine learning models significantly outperformed the conventional CHA₂DS₂-VASc score in predicting 1-year stroke risk, while maintaining interpretability through feature attribution methods. Competing risk analyses revealed no significant differences in stroke or major bleeding risk among treatment strategies after adjustment. This integrated framework highlights the value of combining explainable artificial intelligence with robust epidemiological methods to enhance individualized risk prediction and inform clinical decision-making in AF patients. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-08-28T16:32:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-08-28T16:32:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
致謝 ii 中文摘要 iii Abstract iv 目次 v 圖次 ix 表次 x 第一章 研究背景與動機 1 1.1 心房顫動的流行病學與臨床重要性 1 1.2 中風風險與預測工具之現況 2 1.3 醫療人工智慧與機器學習於臨床風險預測之應用與限制 5 1.4 可解釋人工智慧於醫療領域之重要性 5 1.5 研究動機與臨床需求 7 1.6 研究目的與研究問題 8 第二章 文獻回顧 10 2.1 中風風險預測模型之發展 10 2.2 中風風險預測與新型口服抗凝血藥物處方的臨床意義 13 2.3 機器學習於心房顫動患者中風預測之研究進展 14 2.4 可解釋性方法之比較與臨床解讀 16 2.5 模型臨床可用性評估指標 18 2.5.1 鑑別力 18 2.5.2 校準度 19 2.5.3 整體表現 20 2.5.4 決策曲線分析 20 2.5.5 淨重新分類改善指標 21 2.5.6 次族群分析 22 2.5.7 長期時間效益與可視化解釋 23 2.6 真實世界抗凝血治療策略與政策變遷之影響 23 2.6.1 早期抗凝血治療之臨床爭議 23 2.6.2 給付政策與抗凝血藥物使用模式 24 2.6.3 競爭風險與死亡在心房顫動研究中的角色 24 2.7 本研究相對於既有文獻的創新性及貢獻 25 第三章 研究資料與方法 28 3.1 研究設計 28 3.1.1 中風風險預測模型設計 28 3.1.2 治療策略分析之研究設計 31 3.2 研究對象與資料來源 33 3.2.1 中風風險預測模型研究族群 33 3.2.2 治療策略分析研究族群 37 3.3 變數定義 37 3.3.1 中風風險預測模型變數定義 37 3.3.2 治療策略分析變數定義 38 3.4 資料清理流程 39 3.5 機器學習模型建立流程 40 3.5.1 羅吉斯迴歸模型的特徵選擇、超參數優化與交叉驗證 40 3.5.2 極端梯度提升模型的特徵選擇、超參數優化與交叉驗證 46 3.6 機器學習模型驗證與比較 50 3.7 機器學習模型敏感度分析與子群分析 51 3.8 機器學習模型臨床效益評估 52 3.9 統計分析方法 52 3.9.1 中風風險預測模型分析方法 52 3.9.2 治療策略分析方法 54 3.10 倫理審查與資料安全 55 第四章 研究結果 57 4.1 中風風險預測研究族群與基線特徵 57 4.2 中風風險預測模型訓練與內部驗證結果 60 4.3 中風風險預測外部驗證與多資料集比較結果 65 4.4 中風風險預測模型之校正能力分析 69 4.5 中風風險預測模型臨床效益分析 73 4.6 中風風險預測模型長期風險分層效能分析 76 4.7 中風風險預測模型子群分析與敏感度分析結果 81 4.8 治療策略分析研究族群與基線特徵 81 4.9 不同抗凝治療策略對缺血性中風風險之分析結果 87 4.10 不同抗凝治療策略對重大出血風險之分析結果 92 4.11 性別分層分析、敏感度分析與外部驗證結果 99 第五章 討論 106 5.1 主要發現摘要 106 5.2 與既有文獻比較 107 5.3 臨床意涵與應用潛力 109 5.4 可解釋人工智慧在臨床風險預測的價值 113 5.5 研究優勢 114 5.6 限制與未來研究方向 115 第六章 結論 117 6.1 研究結論 117 6.2 臨床應用建議 117 6.3 未來展望 118 參考文獻 120 附錄 142 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 心房顫動 | - |
| dc.subject | 中風風險預測 | - |
| dc.subject | 可解釋人工智慧 | - |
| dc.subject | 機器學習 | - |
| dc.subject | 競爭風險分析 | - |
| dc.subject | 抗凝血治療 | - |
| dc.subject | atrial fibrillation | - |
| dc.subject | stroke risk prediction | - |
| dc.subject | explainable artificial intelligence | - |
| dc.subject | XAI | - |
| dc.subject | machine learning | - |
| dc.subject | competing risk analysis | - |
| dc.subject | anticoagulant therapy | - |
| dc.title | 新診斷心房顫動患者之中風風險預測與治療策略評估： 結合可解釋人工智慧與競爭風險分析 | zh_TW |
| dc.title | Integrating Explainable Artificial Intelligence and Competing Risk Analysis for Stroke Risk Prediction and Treatment Strategy Evaluation in Newly Diagnosed Atrial Fibrillation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 賴超倫;林先和;李怡姿;張慶國;張淑媛 | zh_TW |
| dc.contributor.oralexamcommittee | Chau-Lun Lai;Hsien-Ho Lin;Yi-Tzu Lee;Chin-Kuo Chang;Sui-Yuan Chang | en |
| dc.subject.keyword | 心房顫動; 中風風險預測; 可解釋人工智慧; 機器學習; 競爭風險分析; 抗凝血治療 | zh_TW |
| dc.subject.keyword | atrial fibrillation; stroke risk prediction; explainable artificial intelligence; XAI; machine learning; competing risk analysis; anticoagulant therapy | en |
| dc.relation.page | 163 | - |
| dc.identifier.doi | 10.6342/NTU202602957 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2026-07-31 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | - |
| dc.date.embargo-lift | 2026-08-29 | - |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
文件中的檔案：
| 檔案 | 描述 | 大小 | 格式 | |
|---|---|---|---|---|
| ntu-114-2.pdf | 36.01 MB | Adobe PDF | 檢視/開啟 | |
| ntu-114-2.pdf | 36.01 MB | Adobe PDF | 檢視/開啟 |
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