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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳秀熙 | zh_TW |
dc.contributor.advisor | Hsiu-Hsi Chen | en |
dc.contributor.author | 許容綺 | zh_TW |
dc.contributor.author | Jung-Chi Hsu | en |
dc.date.accessioned | 2023-03-01T17:04:05Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-05-29 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-02-06 | - |
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Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine. Diabetes Care, 45(1), 204-212. Sheen, Y. J., Hsu, C. C., Jiang, Y. D., Huang, C. N., Liu, J. S., & Sheu, W. H. (2019). Trends in prevalence and incidence of diabetes mellitus from 2005 to 2014 in Taiwan. J Formos Med Assoc, 118 Suppl 2, S66-s73. Skali, H., Shah, A., Gupta, D. K., Cheng, S., Claggett, B., Liu, J., Bello, N., Aguilar, D., Vardeny, O., Matsushita, K., Selvin, E., & Solomon, S. (2015). Cardiac structure and function across the glycemic spectrum in elderly men and women free of prevalent heart disease: the Atherosclerosis Risk In the Community study. Circ Heart Fail, 8(3), 448-454. Stefan, N., Fritsche, A., Schick, F., & Häring, H. U. (2016). Phenotypes of prediabetes and stratification of cardiometabolic risk. Lancet Diabetes Endocrinol, 4(9), 789-798. https://doi.org/10.1016/s2213-8587(16)00082-6 Tabák, A. G., Brunner, E. J., Lindbohm, J. V., Singh-Manoux, A., Shipley, M. J., Sattar, N., & Kivimäki, M. (2022). Risk of Macrovascular and Microvascular Disease in Diabetes Diagnosed Using Oral Glucose Tolerance Test With and Without Confirmation by Hemoglobin A1c: The Whitehall II Cohort Study. Circulation, 146(13), 995-1005. Tabák, A. G., Herder, C., Rathmann, W., Brunner, E. J., & Kivimäki, M. (2012). Prediabetes: a high-risk state for diabetes development. Lancet, 379(9833), 2279-2290. Thomas, M. R., & Lip, G. Y. (2017). Novel Risk Markers and Risk Assessments for Cardiovascular Disease. Circ Res, 120(1), 133-149. Vas, P. R. J., Alberti, K. G., & Edmonds, M. E. (2017). Prediabetes: moving away from a glucocentric definition. Lancet Diabetes Endocrinol, 5(11), 848-849. Wang, J., Sarnola, K., Ruotsalainen, S., Moilanen, L., Lepistö, P., Laakso, M., & Kuusisto, J. (2010). The metabolic syndrome predicts incident congestive heart failure: a 20-year follow-up study of elderly Finns. Atherosclerosis, 210(1), 237-242. Wang, P. E., Wang, T. T., Chiu, Y. H., Yen, A. M., & Chen, T. H. (2006). Evolution of multiple disease screening in Keelung: a model for community involvement in health interventions? J Med Screen, 13 Suppl 1, S54-58. Wang, T. J., Larson, M. G., Levy, D., Vasan, R. S., Leip, E. P., Wolf, P. A., D'Agostino, R. B., Murabito, J. M., Kannel, W. B., & Benjamin, E. J. (2003). Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation, 107(23), 2920-2925. Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244. Welsh, C., Welsh, P., Celis-Morales, C. A., Mark, P. B., Mackay, D., Ghouri, N., Ho, F. K., Ferguson, L. D., Brown, R., Lewsey, J., Cleland, J. G., Gray, S. R., Lyall, D. M., Anderson, J. J., Jhund, P. S., Pell, J. P., McGuire, D. K., Gill, J. M. R., & Sattar, N. (2020). Glycated Hemoglobin, Prediabetes, and the Links to Cardiovascular Disease: Data From UK Biobank. Diabetes Care, 43(2), 440-445. Williams, R., Karuranga, S., Malanda, B., Saeedi, P., Basit, A., Besançon, S., Bommer, C., Esteghamati, A., Ogurtsova, K., Zhang, P., & Colagiuri, S. (2020). Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract, 162, 108072. Yudkin, J. S., & Montori, V. M. (2014). The epidemic of pre-diabetes: the medicine and the politics. Bmj, 349, g4485. Zakeri, R., Chamberlain, A. M., Roger, V. L., & Redfield, M. M. (2013). Temporal relationship and prognostic significance of atrial fibrillation in heart failure patients with preserved ejection fraction: a community-based study. Circulation, 128(10), 1085-1093. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83280 | - |
dc.description.abstract | 背景
第二型糖尿病與胰島素阻抗性與心血管疾病與心律不整有關,同時共病因子也因疾病負荷增加。傳統的統計方法因只評估單一時間點的風險而忽略糖尿病在進展過程前的糖尿病前期所造成的風險。目前沒有相關研究顯示糖尿病前期是否會因代謝症候群增加心房顫動的風險。本篇論文我們使用各種不同的機器學習、多階段馬可夫鏈分析和貝氏網路分析,對心房顫動的風險建立統計模型,以期藉由介入措施達到初級、二級和三級預防的效益。 材料與方法 本篇論文涵蓋三部分。第一部分為分析台大醫院的資料庫,我們使用Cox存活分析、機器學習及隨機存活森林分析等方法,找出對心房顫動高風險的糖尿病前期族群及其相關重要特徵。第二部分分析台灣基隆地區長期社區篩檢追蹤的資料,我們使用馬可夫鏈探索從正常血糖到糖尿病前期到糖尿病的自然病史,並運用貝氏網路探討糖尿病前期患者在代謝症候群隨之進展時對心房顫動的影響。第三部分則進一步使用反事實因果分析模型來探討危險因子。 結果 在台大醫院的資料庫中,我們從Cox模型與Kaplan-Meier分析可知,糖尿病前期的人比正常血糖的人較易罹患心房顫動 (風險比為1.26, 95%信賴區間為1.13-1.40, p值<0.001)。我們比較了各種不同的機器學習方法來預測糖尿病前期的病患發生心房顫動,隨機森林有最高的接收者操作特徵曲線下面積,而當考慮時間因素時,隨機存活森林分析亦比傳統Cox模型有較高的預測能力,並且我們發現心臟衰竭指數與心臟超音波左心房與左心室參數的變化最為重要,其次為腎功能和年齡。非監督性群集分析則將糖尿病前期患者分成三個特徵群,年齡大與心臟衰竭有最高的心房顫動發生率,其次為腎功能較差的族群,最後是肥胖與高血脂的族群。 在社區研究的資料裡,我們使用馬可夫分析並計算從正常血糖發展到糖尿病前期、糖尿病前期到糖尿病、與糖尿病前期返回到正常血糖的每年發生率。每年大約一半的糖尿病前期可回復為正常血糖的人。空腹血糖異常的糖尿病前期當合併葡萄糖耐受不良時有較高的風險從糖尿病前期進展到第二型糖尿病。 貝氏網路分析顯示年紀、尿酸高、男性的糖尿病前期族群較易從無代謝症候群轉變成輕微代謝症候群或代謝症候群,而男性與較差的腎功能較易從代謝症候群階段發生心房顫動;而在調整多重共變項因子後,腎功能差則有正向因果貢獻從代謝症候群到心房顫動。在反事實假說下,若無糖尿病前期與異常腎功能時,相較於有糖尿病前期與異常腎功能的病人,發生心房顫動的風險五年內可以從0.071%降到0.023%,十年內從0.142%到0.047%,十五年內可以從0.212%降到0.070%。 結論 我們發現糖尿病前期有較高的心房顫動的發生率,並且高風險的特徵為心臟衰竭,腎功能差,與年老。在糖尿病前期的階段,有可能心臟結構的變化已經開始。而在社區長期追蹤下,大約有一半的糖尿病前期患者是可逆回正常血糖狀態。同為糖尿病前期的空腹血糖異常與葡萄糖耐受不良在疾病表現上各有不同的特性。當糖尿病前期合併代謝症候群時,男性和異常的腎功能伴隨著較高的風險發生心房顫動。 | zh_TW |
dc.description.abstract | Background:
The incidence and prevalence of diabetes mellitus have increased worldwide. Diabetes is known to raise the risk of atrial fibrillation (AF), however it is unclear if prediabetes, the intermediate stage before diabetes, also increases this risk. The purpose of this research is to determine risk factors for AF in individuals with prediabetes, to understand the natural course of prediabetes and diabetes, and to identify the high-risk prediabetes for AF. Methods: The research has three parts. In Part I, we evaluated individuals with prediabetes by using a medically-integrated database from a Taiwanese hospital and implementing supervised and unsupervised machine learning algorithms for AF prediction as well as time-to-event analysis. The machine learning techniques coverage logistic regression, decision tree, random forest, polynomial support vector machine (SVM), and artificial neural networks (ANN). In Part II, we examined the dynamic process of diabetes utilizing community data and developed a Bayesian network model from free of metabolic disorder (FMD), mild metabolic disorder (MMD), and metabolic syndrome (MetS) to atrial fibrillation (AF) utilizing the Keelung community-based integrated screening program. In Part III, we investigated the risk distribution for AF using Bayesian network counterfactual analysis. Results: We evaluated data from 174,835 National Taiwan University Hospital patients over a median follow-up period of 47.6 months. Prediabetes is independently associated with a significantly higher incidence of AF compared to normal glucose test (NGT) individuals (Hazard ratio [HR] 1.26, 95% confidence interval [CI) 1.13-1.40, p<0.001). This association was particularly pronounced in individuals with HbA1c levels greater than 5.5%. Elevated N-terminal natriuretic peptide (NT-proBNP) level and changed cardiac structure were the most significant factors, followed by worse renal function and advanced age. Hierarchical cluster analysis indicated three phenotypes of prediabetes with significantly different risks of atrial fibrillation (log-rank p<0.001). Between 1999 and 2009, 107,501 individuals were enrolled in the Keelung community-based screening program. Using a continuous-time Markov model, the diabetes transition rate was 14.4% from impaired fasting glucose (IFG) with impaired glucost tolerance (IGT) and 8.6% from IFG without IGT in males, and 13.0% from IFG with IGT and 7.9% from IFG without IGT in women. Approximately fifty percent of IFG participants reverted to NGT annually. Bayesian analysis revealed that older age, male gender, and abnormal renal function increased AF in prediabetes, whereas smoking and alcohol intake contributed to the transition from MMD to MetS. When hyperglycemia and elevated BUN were absent, the risk of AF decreased from 0.071% to 0.023% in five years, 0.142% to 0.047% in ten years, and 0.212% to 0.070% in fifteen years. Conclusions: Prediabetes increases the risk of atrial fibrillation. Important contributors to this increased risk include alterations in left heart anatomy, which may already be present in the prediabetes stage. Distinct characteristics were observed between IFG and IGT in a community-based cohort study. Prediabetes and metabolic syndrome, in conjunction with being male and having impaired renal function, have been associated with a higher incidence of AF. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-03-01T17:04:05Z No. of bitstreams: 0 | en |
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dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 中文摘要 III ABSTRACT V CONTENTS VIII LIST OF FIGURES XII LIST OF TABLES XV Chapter 1 Rationales for Machine Learning Design in Elucidating the Complex Disease Process of Cardiovascular Disease with Emphasis on Prediabetes and Atrial fibrillation 1 1.1 Machine learning model for complex disease taxonomy 1 1.2 Diabetes Being an Important Modifier in Atrial Fibrillation 4 1.3 The Trajectory of Dynamic Diabetes Stages 6 1.4 Prediabetes and Its Associated Cardiovascular Disease 7 1.5 Objectives and Research Novelty 8 Chapter 2 Literature Review of Epidemiology, Mechanisms, Prognosis of Atrial Fibrillation and Cardiovascular Disease in Individuals with Prediabetes and Diabetes 10 2.1 Epidemiology and Prognosis of Type 2 Diabetes Mellitus 10 2.2 The Mechanisms of Atrial Fibrillation in Patients with Diabetes 12 2.3 Prediabetes with Insulin Resistance is An Intermediate Stages for Diverse Cardiovascular Disease 16 2.4 Prediction of Cardiovascular Disease with Artificial Intelligence 22 Chapter 3 Hypothesis and Data Source 26 3.1 Hospital-integrated Study Population and Data Collection 26 3.2 Multiple Screening Program's Community-Based Longitudinal Survey 30 3.3 Enrollment and Glucose Testing of the Community 34 3.4 Metabolic Syndrome and Refined Classification 35 Chapter 4 Methodology 38 4.1 Logistic Regression 40 4.2 Decision Tree 41 4.3 Random Forest 42 4.4 Support Vector Machine 43 4.5 Artificial Neural Network 44 4.6 Random Survival Forest 46 4.7 Hierarchical Clustering 48 4.8. Multi-state Markov Analysis 50 4.8.1 The Continuous Time Markov Model for The Natural History of DM 51 4.8.2 Individualized Risk Stratification Using Multistate Markov Regression 56 4.8.3 Dynamics of Metabolic Syndrome and AF 58 4.9 Bayesian Network 60 4.10 Counterfactual Analysis 61 Chapter 5 Results 63 5.1 The Relationship of Prediabetes and Atrial Fibrillation 63 5.2 Semiparametric Cox Proportional Hazards Regression Analysis 69 5.3 Parametric Accelerated Failure Time Models 76 5.4 Logistic Regression for AF in Prediabetes 77 5.5 Decision Tree for AF in Prediabetes 80 5.6 Random Forest for AF in Prediabetes 83 5.7 Support Vector Machine for AF in Prediabetes 85 5.8 Artificial Neural Network for AF in Prediabetes 87 5.9 Random Survival Forest for AF in Prediabetes 91 5.10 Comparison of Random Survival Forest and Cox Regression 96 5.11 Summary for AUCs in Predicting AF in Prediabetes 99 5.12 Three Distinct Phenotype of Prediabetes for AF 101 5.13 Incidence and Prevalence of Prediabetes and Diabetes in the KCIS Program 106 5.14 Natural Course of Diabetes Proregression with Markov model 114 5.15 Incidence of AF in KCIS Program Community-based Prediabetes 122 5.16 Natural Course of Metabolic syndrome Proregression and AF Development with Bayesian Network 128 5.17 Counterfactual Analysis for AF 133 Chapter 6 Discussion 135 6.1 Non-identical Presentation of IFG and IGT 135 6.2 Clinical Pearls of AF in Prediabetes 136 6.3 Natural Course of Diabetes Proregression with Markov model 138 6.4 Interpretability and Explainability of Bayesian Network and Random Survival Forest 141 6.5 Perspective 141 Conclusion 142 Reference 143 | - |
dc.language.iso | en | - |
dc.title | 以整合機器學習設計探索與心血管疾病相關糖尿病動態路徑 | zh_TW |
dc.title | Synthetic Design of Machine Learning for Cardiovascular Disease Process with Selected Pathways on Dynamics of Type 2 Diabetes Mellitus | en |
dc.title.alternative | Synthetic Design of Machine Learning for Cardiovascular Disease Process with Selected Pathways on Dynamics of Type 2 Diabetes Mellitus | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 博士 | - |
dc.contributor.coadvisor | 林亮宇 | zh_TW |
dc.contributor.coadvisor | Lian-Yu Lin | en |
dc.contributor.oralexamcommittee | 吳卓鍇;陳祈玲;潘信良;莊紹源;嚴明芳 | zh_TW |
dc.contributor.oralexamcommittee | Cho-Kai WU;Chi-Ling Chen;Shin-Liang Pan;Shao-Yuan Chuang;Ming-Fang Yen | en |
dc.subject.keyword | 糖尿病,糖尿病前期,心房顫動,馬可夫鏈分析,機器學習,隨機存活森林,貝氏網路,因果分析, | zh_TW |
dc.subject.keyword | diabetes mellitus,prediabetes,atrial fibrillation,multi-state Markov analysis,machine learning,random survival forest,Bayesian network,counterfactual analysis, | en |
dc.relation.page | 163 | - |
dc.identifier.doi | 10.6342/NTU202300287 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-02-07 | - |
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 | - |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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