智慧共同照護之實現: 以資料驅動為基礎之 AI 糖尿病個案管理模式

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姓名	李侑霖(Yu-Lin Li) 查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	智慧共同照護之實現: 以資料驅動為基礎之 AI 糖尿病個案管理模式 (Smart Shared Care:Data-driven AI-based Diabetes Case Management Model)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 (2028-7-7以後開放)
摘要(中)	近年生活習慣與飲食型態的改變，糖尿病的患病人口數逐年上升，且糖尿病是無法治癒的。現今台灣政府積極推廣「糖尿病共同照護網」的積極支援性照護模式，以往文獻指出共同照護的互相支持確能有效減少糖尿病併發症的發生，雖立意良好，但目前我國成效不佳。探究原因可能是廣泛性的收案照護，使得照護模式下的品質不若想像中良好。因此，若以病人為中心做考量，來了解其是否適宜加入共同照護網，才能真正精準地用有效的資源幫助需要幫助的糖尿病人。本研究共設計了三階段的實驗，實驗方法採用機器學習及深度學習中的五種分類器。第一階段訓練並預測不同糖尿病人血糖控制是否穩定;第二階段訓練並預測不同糖尿病人是否需接受積極照護;第三階段則是利用前兩階段模型之重要特徵變數，預測不同糖尿病人是否需加入糖尿病共同照護網。於實驗最後，也會以評估指標，來評估本研究所產出之模型效能。研究結果指出，最佳預測血糖穩定性模型為 RF，準確率達 90%;最佳預測糖尿病人積極治療必要性模型為 XGBoost，準確率 73%;最佳預測糖尿病人加入糖尿病共同照護網必要性模型為 Random Forest，準確率 86%。藉由本研究，可讓臨床端精準地判斷病人加入糖尿病共同照護網的必要性，並針對病人血糖控制穩定性、藥物或胰島素積極治療必要性提供照護的建議指引，以建構完整的智慧共同照護模式。
摘要(英)	With the change of people′s living and eating habits in recent years, the number of people with diabetes are increasing year by year, and diabetes cannot be cured. Nowadays, Taiwan government is promoting the "Diabetes Shared Care Network", and previous studies have shown that "Diabetes Shared Care Network" can effectively reduce the occurrence of diabetes complications. However, the current effectiveness of this initiative in Taiwan is not satisfactory. The possible reason for this is the broad-based inclusion of patients, which may result in a lower quality of care than anticipated. Therefore, it is essential to consider the patient′s perspective to determine whether they are suitable for inclusion in the "Diabetes Shared Care Network". We designed a three-stage experiment. All experiments use five classifiers to construct the predictive models. In the first stage, the models are trained to predict the stability of blood glucose control in different individuals with diabetes. In the second stage, we use machine learning techniques to find out which diabetic patients need curative care. In the last stage, we use the results of the above two experiments to predict who should join the "Diabetes Shared Care Network". At the end of the experiments, the performance of the models developed in this study will be evaluated using various evaluation metrics. The research findings indicate that the Random Forest (RF) model achieved the highest accuracy of 90% in predicting blood glucose stability. The XGBoost model was found to be the best in predicting the necessity of curative care for individuals with diabetes, with an accuracy of 73%. Additionally, the Random Forest model performed the best in predicting the necessity of individuals joining the Diabetes Shared Care Network, with an accuracy of 86%. Through this study, we can make precise judgments regarding the necessity of patients joining the "Diabetes Shared Care Network". The findings also provide guidance on blood glucose control stability and the need for medication or insulin therapy, facilitating the development of a comprehensive intelligent shared care model.
關鍵字(中)	★ 機器學習 ★ 深度學習 ★ 糖尿病 ★ 共同照護 ★ 智慧醫療 ★ 醫療資訊管理	關鍵字(英)	★ Machine Learning ★ Deep Learning ★ Diabetes Mellitus ★ Co-Care ★ Smart Healthcare ★ Healthcare Information Management
論文目次	中文摘要 i ABSTRACT ii 致謝 iii 目錄 iv 圖目錄 vii 表目錄 viii 第一章緒論 1 1-1 研究背景 1 1-2 研究動機 6 1-3 研究目的 8 1-4 論文架構 9 第二章文獻探討 11 2-1 糖尿病與治療照護 11 2-1-1 糖尿病類型 11 2-1-2 糖尿病治療 12 2-1-3 糖尿病共同照護網 14 2-2 血糖控制影響因素 17 2-3 機器學習於糖尿病 21 2-4 深度學習於糖尿病 24 第三章研究方法 27 3-1 個案醫院介紹 28 3-2 資料蒐集對象 29 3-3 資料集介紹 30 3-4 資料前處理 32 3-5 機器學習及深度學習技術介紹 41 3-5-1 支援向量機（Support Vector Machine, SVM） 41 3-5-2 決策樹（Decision Tree, DT） 43 3-5-3 隨機森林（Random Forest, RF） 44 3-5-4 極限梯度提升樹（eXtreme Gradient Boosting, XGBoost） 45 3-5-5 神經網路（Neural Network, NN） 46 3-6 實驗設計及模型評估指標 47 3-6-1 階段一：糖尿病人血糖穩定預測模型 48 3-6-2 階段二：糖尿病人積極治療必要性決策支援模型 49 3-6-3 階段三：糖尿病人加入糖尿病共同照護網必要性決策支援模型 50 3-6-4 特徵選取 51 3-6-5 模型驗證與評估 52 3-7 倫理審查 54 第四章研究結果分析 55 4-1 資料描述性統計 55 4-1-1 類別型資料 55 4-1-2 連續型資料 58 4-2 階段一結果 61 4-3 階段二結果 64 4-4 階段三結果 68 4-5 綜合實驗結果討論 72 第五章研究結論與建議 73 5-1 結論 73 5-1-1 糖尿病人血糖穩定性預測模型 73 5-1-2 糖尿病人積極治療或加入照護網必要性之決策支援模型 74 5-2 研究貢獻 75 5-3 研究限制 76 5-4 未來研究方向與建議 77 參考文獻 78
參考文獻	Allen, A., Iqbal, Z., Green-Saxena, A., Hurtado, M., Hoffman, J., Mao, Q., & Das, R. (2022). Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Research and Care, 10(1), e002560. https://doi.org/10.1136/bmjdrc-2021-002560 Amarnath, S., Selvamani, M., & Varadarajan, V. (2021). Prognosis Model for Gestational Diabetes Using Machine Learning Techniques. Sensors and Materials, 33, 3011. https://doi.org/10.18494/SAM.2021.3119 American Diabetes Association. (2004). Gestational diabetes mellitus. Diabetes Care, 27 Suppl 1, S88-90. https://doi.org/10.2337/diacare.27.2007.s88 American Diabetes Association. (2018). Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care, 41(5), 917–928. https://doi.org/10.2337/dci18-0007 Arcadu, F., Benmansour, F., Maunz, A., Willis, J., Haskova, Z., & Prunotto, M. (2019). Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digital Medicine, 2, 92. https://doi.org/10.1038/s41746-019-0172-3 Atkinson, M. A., & Eisenbarth, G. S. (2001). Type 1 diabetes: New perspectives on disease pathogenesis and treatment. The Lancet, 358(9277), 221–229. https://doi.org/10.1016/S0140-6736(01)05415-0 Azar, A. T., Elshazly, H. I., Hassanien, A. E., & Elkorany, A. M. (2014). A random forest classifier for lymph diseases. Computer Methods and Programs in Biomedicine, 113(2), 465–473. https://doi.org/10.1016/j.cmpb.2013.11.004 Bae, J. C., Suh, S., Jin, S., Kim, S. W., Hur, K. Y., Kim, J. H., Min, Y., Lee, M., Lee, M. K., Jeon, W. S., Lee, W. Y., & Kim, K. (2014). Hemoglobin A1c values are affected by hemoglobin level and gender in non‐anemic Koreans. Journal of Diabetes Investigation, 5(1), 60–65. https://doi.org/10.1111/jdi.12123 Berthold, H., & Gouni-Berthold, I. (2015). Physician variability in managing type 2 diabetes mellitus: Reasons and potential consequences. Diabetes/Metabolism Research and Reviews, 31(8), 154–162. https://doi.org/10.1002/dmrr.2597 Buchanan, T. A., & Xiang, A. H. (2005). Gestational diabetes mellitus. Journal of Clinical Investigation, 115(3), 485–491. https://doi.org/10.1172/JCI200524531 CDC. (2021, March 25). Diabetic Ketoacidosis. Centers for Disease Control and Prevention. https://www.cdc.gov/diabetes/basics/diabetic-ketoacidosis.html Chaker, L., Ligthart, S., Korevaar, T. I. M., Hofman, A., Franco, O. H., Peeters, R. P., & Dehghan, A. (2016). Thyroid function and risk of type 2 diabetes: A population-based prospective cohort study. BMC Medicine, 14, 150. https://doi.org/10.1186/s12916-016-0693-4 Chang, V., Ganatra, M. A., Hall, K., Golightly, L., & Xu, Q. A. (2022). An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthcare Analytics, 2, 100118. https://doi.org/10.1016/j.health.2022.100118 Chen, C.-H., Ma, S.-H., Hu, S.-Y., Chang, C.-M., Chiang, J.-H., Hsieh, V. C.-R., Yen, D. H.-T., How, C.-K., & Hsieh, M.-S. (2018). Diabetes Shared Care Program (DSCP) and risk of infection mortality: A nationwide cohort study using administrative claims data in Taiwan. BMJ Open, 8(7), e021382. https://doi.org/10.1136/bmjopen-2017-021382 Chen, L., Magliano, D., & Zimmet, P. (2011). Chen, L, Magliano, DJ and Zimmet, PZ. The worldwide epidemiology of type 2 diabetes mellitus-present and future perspectives. Nat Rev Endocrinol 8: 228-236. Nature Reviews. Endocrinology, 8, 228–236. https://doi.org/10.1038/nrendo.2011.183 Chowdhury, T. A. (2019). Post-transplant diabetes mellitus. Clinical Medicine, 19(5), 392–395. https://doi.org/10.7861/clinmed.2019-0195 Clément, P., Goff, M., Thiébaut, R., Dartigues, J., & Helmer, C. (2011). Effectiveness of disease-management programs for improving diabetes care: A meta-analysis. CMAJ : Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 183, E115-27. https://doi.org/10.1503/cmaj.091786 Cruz-Vega, I., Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J. de J., & Ramirez-Cortes, J. M. (2020). Deep Learning Classification for Diabetic Foot Thermograms. Sensors (Basel, Switzerland), 20(6), 1762. https://doi.org/10.3390/s20061762 Davies, M. J., D’Alessio, D. A., Fradkin, J., Kernan, W. N., Mathieu, C., Mingrone, G., Rossing, P., Tsapas, A., Wexler, D. J., & Buse, J. B. (2018). Management of Hyperglycemia in Type 2 Diabetes, 2018. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care, 41(12), 2669–2701. https://doi.org/10.2337/dci18-0033 DeFronzo, R. A. (2009). From the Triumvirate to the Ominous Octet: A New Paradigm for the Treatment of Type 2 Diabetes Mellitus. Diabetes, 58(4), 773–795. https://doi.org/10.2337/db09-9028 Dong, Z., Wang, Q., Ke, Y., Zhang, W., Hong, Q., Liu, C., Liu, X., Yang, J., Xi, Y., Shi, J., Zhang, L., Zheng, Y., Lv, Q., Wang, Y., Wu, J., Sun, X., Cai, G., Qiao, S., Yin, C., … Chen, X. (2022). Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records. Journal of Translational Medicine, 20(1), 143. https://doi.org/10.1186/s12967-022-03339-1 Doorn, W. P. T. M. van, Foreman, Y. D., Schaper, N. C., Savelberg, H. H. C. M., Koster, A., Kallen, C. J. H. van der, Wesselius, A., Schram, M. T., Henry, R. M. A., Dagnelie, P. C., Galan, B. E. de, Bekers, O., Stehouwer, C. D. A., Meex, S. J. R., & Brouwers, M. C. G. J. (2021). Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study. PLOS ONE, 16(6), e0253125. https://doi.org/10.1371/journal.pone.0253125 Eisenbarth, G. S. (1986). Type I diabetes mellitus. A chronic autoimmune disease. The New England Journal of Medicine, 314(21), 1360–1368. https://doi.org/10.1056/NEJM198605223142106 Elhadd, T., Mall, R., Bashir, M., Palotti, J., Fernandez-Luque, L., Farooq, F., Mohanadi, D. A., Dabbous, Z., Malik, R. A., & Abou-Samra, A. B. (2020). Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study). Diabetes Research and Clinical Practice, 169, 108388. https://doi.org/10.1016/j.diabres.2020.108388 ElSayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., Collins, B. S., Hilliard, M. E., Isaacs, D., Johnson, E. L., Kahan, S., Khunti, K., Leon, J., Lyons, S. K., Perry, M. L., Prahalad, P., Pratley, R. E., Seley, J. J., Stanton, R. C., … on behalf of the American Diabetes Association. (2022). 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2023. Diabetes Care, 46(Supplement_1), S19–S40. https://doi.org/10.2337/dc23-S002 Flannick, J., Johansson, S., & Njølstad, P. R. (2016). Common and rare forms of diabetes mellitus: Towards a continuum of diabetes subtypes. Nature Reviews. Endocrinology, 12(7), 394–406. https://doi.org/10.1038/nrendo.2016.50 Franz, M., Boucher, J., Rutten-Ramos, S., & VanWormer, J. (2015). Lifestyle Weight-Loss Intervention Outcomes in Overweight and Obese Adults with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. Journal of the Academy of Nutrition and Dietetics, 115. https://doi.org/10.1016/j.jand.2015.02.031 Ganie, S. M., & Malik, M. B. (2022). An ensemble Machine Learning approach for predicting Type-II diabetes mellitus based on lifestyle indicators. Healthcare Analytics, 2, 100092. https://doi.org/10.1016/j.health.2022.100092 Ghaith, N., Malaeb, B., Itani, R., Alnafea, M., & Al Faraj, A. (2021). Correlation of Kidney Size on Computed Tomography with GFR, Creatinine and HbA1C for an Accurate Diagnosis of Patients with Diabetes and/or Chronic Kidney Disease. Diagnostics, 11(5), Article 5. https://doi.org/10.3390/diagnostics11050789 Gopi, A. P., Jyothi, R. N. S., Narayana, V. L., & Sandeep, K. S. (2023). Classification of tweets data based on polarity using improved RBF kernel of SVM. International Journal of Information Technology, 15(2), 965–980. https://doi.org/10.1007/s41870-019-00409-4 Habibi, S., Ahmadi, M., & Alizadeh, S. (2015). Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining. Global Journal of Health Science, 7(5), 304–310. https://doi.org/10.5539/gjhs.v7n5p304 Hall, S. J., Samuel, L. M., & Murchie, P. (2011). Toward shared care for people with cancer: Developing the model with patients and GPs. Family Practice, 28(5), 554–564. https://doi.org/10.1093/fampra/cmr012 Hawale, D., Ambad, R., Hadke, S., & A, A. (2021). Correlation of HBA1C with UACR and Serum Creatinine Level in Type 2 Diabetes Mellitus. International Journal of Current Research and Review, 13, 188–192. https://doi.org/10.31782/IJCRR.2021.131120 Hickman, M., Drummond, N., & Grimshaw, J. (1994). A taxonomy of shared care for chronic disease. Journal of Public Health, 16(4), 447–454. https://doi.org/10.1093/oxfordjournals.pubmed.a043026 Hober, D., & Sauter, P. (2010). Pathogenesis of type 1 diabetes mellitus: Interplay between enterovirus and host. Nature Reviews Endocrinology, 6(5), Article 5. https://doi.org/10.1038/nrendo.2010.27 Hod, M., Kapur, A., Sacks, D. A., Hadar, E., Agarwal, M., Di Renzo, G. C., Roura, L. C., McIntyre, H. D., Morris, J. L., & Divakar, H. (2015). The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: A pragmatic guide for diagnosis, management, and care#. International Journal of Gynecology & Obstetrics, 131(S3), S173–S211. https://doi.org/10.1016/S0020-7292(15)30033-3 Hong, J. W., Noh, J. H., & Kim, D.-J. (2018). Association between White Blood Cell Counts within Normal Range and Hemoglobin A1c in a Korean Population. Endocrinology and Metabolism, 33(1), 79–87. https://doi.org/10.3803/EnM.2018.33.1.79 Huang, S.-H., Huang, P.-J., Li, J.-Y., Su, Y.-D., Lu, C.-C., & Shih, C.-L. (2021). Hemoglobin A1c Levels Associated with Age and Gender in Taiwanese Adults without Prior Diagnosis with Diabetes. International Journal of Environmental Research and Public Health, 18(7), 3390. https://doi.org/10.3390/ijerph18073390 Hussain, A., Ali, I., Ijaz, M., & Rahim, A. (2017). Correlation between hemoglobin A1c and serum lipid profile in Afghani patients with type 2 diabetes: Hemoglobin A1c prognosticates dyslipidemia. Therapeutic Advances in Endocrinology and Metabolism, 8(4), 51–57. https://doi.org/10.1177/2042018817692296 Ikram, S., Priya, V., Balakrishnan, A., Cheng, X., Ghalib, Dr. M., & Shankar, A. (2022). Prediction of IIoT traffic using a modified whale optimization approach integrated with random forest classifier. The Journal of Supercomputing, 78, 1–32. https://doi.org/10.1007/s11227-021-04284-4 Imperatore, G., Mayer-Davis, E. J., Orchard, T. J., & Zhong, V. W. (2018). Prevalence and Incidence of Type 1 Diabetes Among Children and Adults in the United States and Comparison With Non-U.S. Countries. In C. C. Cowie, S. S. Casagrande, A. Menke, M. A. Cissell, M. S. Eberhardt, J. B. Meigs, E. W. Gregg, W. C. Knowler, E. Barrett-Connor, D. J. Becker, F. L. Brancati, E. J. Boyko, W. H. Herman, B. V. Howard, K. M. V. Narayan, M. Rewers, & J. E. Fradkin (Eds.), Diabetes in America (3rd ed.). National Institute of Diabetes and Digestive and Kidney Diseases (US). http://www.ncbi.nlm.nih.gov/books/NBK568003/ International Diabetes Federation. (2021). IDF Diabetes Atlas 10th edition. https://diabetesatlas.org/ Kaaja, R. J., & Greer, I. A. (2005). Manifestations of Chronic Disease During Pregnancy. JAMA, 294(21), 2751–2757. https://doi.org/10.1001/jama.294.21.2751 Kannadasan, K., Edla, D. R., & Kuppili, V. (2019). Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clinical Epidemiology and Global Health, 7(4), 530–535. https://doi.org/10.1016/j.cegh.2018.12.004 Kim, C., Newton, K. M., & Knopp, R. H. (2002). Gestational Diabetes and the Incidence of Type 2 Diabetes: A systematic review. Diabetes Care, 25(10), 1862–1868. https://doi.org/10.2337/diacare.25.10.1862 Kim, S., & Lee, H. (2022). Customer Churn Prediction in Influencer Commerce: An Application of Decision Trees. Procedia Computer Science, 199, 1332–1339. https://doi.org/10.1016/j.procs.2022.01.169 Kuo, I.-C., Lin, H. Y.-H., Niu, S.-W., Lee, J.-J., Chiu, Y.-W., Hung, C.-C., Hwang, S.-J., & Chen, H.-C. (2018). Anemia modifies the prognostic value of glycated hemoglobin in patients with diabetic chronic kidney disease. PLoS ONE, 13(6), e0199378. https://doi.org/10.1371/journal.pone.0199378 Li, X., Sun, L., Ling, M., & Peng, Y. (2023). A survey of graph neural network based recommendation in social networks. Neurocomputing, 549, 126441. https://doi.org/10.1016/j.neucom.2023.126441 Lin, Y.-T., Huang, W.-L., Wu, H.-P., Chang, M.-P., & Chen, C.-C. (2021). Association of Mean and Variability of HbA1c with Heart Failure in Patients with Type 2 Diabetes. Journal of Clinical Medicine, 10(7), Article 7. https://doi.org/10.3390/jcm10071401 Lorig, K. R., Sobel, D. S., Stewart, A. L., Brown, B. W., Bandura, A., Ritter, P., Gonzalez, V. M., Laurent, D. D., & Holman, H. R. (1999). Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: A randomized trial. Medical Care, 37(1), 5–14. https://doi.org/10.1097/00005650-199901000-00003 Lv, X., Qiao, W., Leng, Y., Wu, L., & Zhou, Y. (2017). Impact of diabetes mellitus on clinical outcomes of pancreatic cancer after surgical resection: A systematic review and meta-analysis. PLoS ONE, 12(2), e0171370. https://doi.org/10.1371/journal.pone.0171370 Matsushita, Y., Takeda, N., Nakamura, Y., Yoshida-Hata, N., Yamamoto, S., Noda, M., Yokoyama, T., Mizoue, T., & Nakagawa, T. (2020). A Comparison of the Association of Fasting Plasma Glucose and HbA1c Levels with Diabetic Retinopathy in Japanese Men. Journal of Diabetes Research, 2020, e3214676. https://doi.org/10.1155/2020/3214676 Mikkola, I., Hagnäs, M., Hartsenko, J., Kaila, M., & Winell, K. (2020). A Personalized Care Plan Is Positively Associated With Better Clinical Outcomes in the Care of Patients With Type 2 Diabetes: A Cross-Sectional Real-Life Study. Canadian Journal of Diabetes, 44(2), 133–138. https://doi.org/10.1016/j.jcjd.2019.05.003 Motaib, I., Aitlahbib, F., Fadil, A., Z.Rhmari Tlemcani, F., Elamari, S., Laidi, S., & Chadli, A. (2022). Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models. Diabetes Research and Clinical Practice, 190, 109982. https://doi.org/10.1016/j.diabres.2022.109982 National Institute of Diabetes and Digestive and Kidney Diseases. (2017, May). Gestational Diabetes. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/diabetes/overview/what-is-diabetes/gestational/all-content Nozawa, K., Ikeda, M., & Kikuchi, S. (2022). Association Between HbA1c Levels and Diabetic Peripheral Neuropathy: A Case–Control Study of Patients with Type 2 Diabetes Using Claims Data. Drugs - Real World Outcomes, 9(3), 403–414. https://doi.org/10.1007/s40801-022-00309-3 Ogunyemi, O., & Kermah, D. (2015). Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records. AMIA Annual Symposium Proceedings, 2015, 983–990. Palimkar, P., Shaw, R. N., & Ghosh, A. (2022). Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach. In M. Bianchini, V. Piuri, S. Das, & R. N. Shaw (Eds.), Advanced Computing and Intelligent Technologies (pp. 219–244). Springer. https://doi.org/10.1007/978-981-16-2164-2_19 Park, S., Lee, H. s., & Kim, J. (2017). Seed growing for interactive image segmentation using SVM classification with geodesic distance. Electronics Letters, 53(1), 22–24. https://doi.org/10.1049/el.2016.3919 Patalas-Maliszewska, J., Łosyk, H., & Rehm, M. (2022). Decision-Tree Based Methodology Aid in Assessing the Sustainable Development of a Manufacturing Company. Sustainability, 14(10), Article 10. https://doi.org/10.3390/su14106362 Phyo Phyo San, null, Sai Ho Ling, null, & Nguyen, H. T. (2016). Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2016, 3503–3506. https://doi.org/10.1109/EMBC.2016.7591483 Poongodi, M., Malviya, M., Kumar, C., Hamdi, M., Vijayakumar, V., Nebhen, J., & Alyamani, H. (2022). New York City taxi trip duration prediction using MLP and XGBoost. International Journal of System Assurance Engineering and Management, 13(1), 16–27. https://doi.org/10.1007/s13198-021-01130-x Prabhu, P., & Selvabharathi, S. (2019). Deep Belief Neural Network Model for Prediction of Diabetes Mellitus. 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), 138–142. https://doi.org/10.1109/ICISPC.2019.8935838 Qi, Y. (2012). Random Forest for Bioinformatics. In C. Zhang & Y. Ma (Eds.), Ensemble Machine Learning: Methods and Applications (pp. 307–323). Springer US. https://doi.org/10.1007/978-1-4419-9326-7_11 Qummar, S., Khan, F. G., Shah, S., Khan, A., Din, A., & Gao, J. (2020). Deep Learning Techniques for Diabetic Retinopathy Detection. Current Medical Imaging, 16(10), 1201–1213. https://doi.org/10.2174/1573405616666200213114026 Rahman, M., Islam, D., Mukti, R. J., & Saha, I. (2020). A deep learning approach based on convolutional LSTM for detecting diabetes. Computational Biology and Chemistry, 88, 107329. https://doi.org/10.1016/j.compbiolchem.2020.107329 Rehman, M. U., Shafique, A., Khalid, S., Driss, M., & Rubaiee, S. (2021). Future Forecasting of COVID-19: A Supervised Learning Approach. Sensors (Basel, Switzerland), 21(10), 3322. https://doi.org/10.3390/s21103322 Richhariya, B., Tanveer, M., & Rashid, A. H. (2020). Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomedical Signal Processing and Control, 59, 101903. https://doi.org/10.1016/j.bspc.2020.101903 Ryu, K., Lee, J., Batbaatar, E., Lee, J., Choi, K., & Cha, H. (2020). A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes. Applied Sciences, 10, 421. https://doi.org/10.3390/app10010421 Sanz, M., Ceriello, A., Buysschaert, M., Chapple, I., Demmer, R. T., Graziani, F., Herrera, D., Jepsen, S., Lione, L., Madianos, P., Mathur, M., Montanya, E., Shapira, L., Tonetti, M., & Vegh, D. (2018). Scientific evidence on the links between periodontal diseases and diabetes: Consensus report and guidelines of the joint workshop on periodontal diseases and diabetes by the International Diabetes Federation and the European Federation of Periodontology. Journal of Clinical Periodontology, 45(2), 138–149. https://doi.org/10.1111/jcpe.12808 Sebern, M. D., & Woda, A. (2012). Shared Care Dyadic Intervention: Outcome Patterns for Heart Failure Care Partners. Western Journal of Nursing Research, 34(3), 289–316. https://doi.org/10.1177/0193945911399088 Sen-Crowe, B., Sutherland, M., McKenney, M., & Elkbuli, A. (2021). A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic. Journal of Surgical Research, 260, 56–63. https://doi.org/10.1016/j.jss.2020.11.062 Shahin, O. R., Alshammari, H. H., Alzahrani, A. A., Alkhiri, H., & Taloba, A. I. (2023). A robust deep neural network framework for the detection of diabetes. Alexandria Engineering Journal, 74, 715–724. https://doi.org/10.1016/j.aej.2023.05.072 Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., & Sakharkar, M. K. (2016). Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker Insights, 11, 95–104. https://doi.org/10.4137/BMI.S38440 Shimpi, P., Shah, S., Shroff, M., & Godbole, A. (2017). A machine learning approach for the classification of cardiac arrhythmia. 2017 International Conference on Computing Methodologies and Communication (ICCMC), 603–607. https://doi.org/10.1109/ICCMC.2017.8282537 Singh, V., Poonia, R., Kumar, S., Dass, P., Agarwal, P., Bhatnagar, V., & Raja, L. (2020). Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine. Journal of Discrete Mathematical Sciences and Cryptography, 23. https://doi.org/10.1080/09720529.2020.1784535 Sivaraman, S. C., Vinnamala, S., & Jenkins, D. (2013). Gestational Diabetes and Future Risk of Diabetes. Journal of Clinical Medicine Research, 5(2), 92–96. https://doi.org/10.4021/jocmr1201w Solis-Herrera, C., Triplitt, C., Reasner, C., DeFronzo, R. A., & Cersosimo, E. (2000). Classification of Diabetes Mellitus. In K. R. Feingold, B. Anawalt, M. R. Blackman, A. Boyce, G. Chrousos, E. Corpas, W. W. de Herder, K. Dhatariya, K. Dungan, J. Hofland, S. Kalra, G. Kaltsas, N. Kapoor, C. Koch, P. Kopp, M. Korbonits, C. S. Kovacs, W. Kuohung, B. Laferrère, … D. P. Wilson (Eds.), Endotext. MDText.com, Inc. http://www.ncbi.nlm.nih.gov/books/NBK279119/ Størling, J., & Pociot, F. (2017). Type 1 Diabetes Candidate Genes Linked to Pancreatic Islet Cell Inflammation and Beta-Cell Apoptosis. Genes, 8(2), 72. https://doi.org/10.3390/genes8020072 Sugondo, A. T., Ardiany, D., Nuswantoro, D., & Notopuro, P. B. (2019). Relationship between HbA1c Levels with eGFR and Blood Pressure in Type 2 Diabetes Mellitus Patients in the Department of Internal Medicine Dr. Soetomo General Hospital Surabaya. Biomolecular and Health Science Journal, 2(2), Article 2. https://doi.org/10.20473/bhsj.v2i2.14956 The International Expert Committee. (2009). International Expert Committee Report on the Role of the A1C Assay in the Diagnosis of Diabetes. Diabetes Care, 32(7), 1327–1334. https://doi.org/10.2337/dc09-9033 Tsao, H.-Y., Chan, P.-Y., & Su, E. C.-Y. (2018). Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms. BMC Bioinformatics, 19(Suppl 9), 283. https://doi.org/10.1186/s12859-018-2277-0 Unwin. (2002). Impaired glucose tolerance and impaired fasting glycaemia: The current status on definition and intervention. Diabetic Medicine, 19(9), 708–723. https://doi.org/10.1046/j.1464-5491.2002.00835.x Vaxillaire, M., Bonnefond, A., & Froguel, P. (2012). The lessons of early-onset monogenic diabetes for the understanding of diabetes pathogenesis. Best Practice & Research. Clinical Endocrinology & Metabolism, 26(2), 171–187. https://doi.org/10.1016/j.beem.2011.12.001 Vigersky, R. A., & McMahon, C. (2019). The Relationship of Hemoglobin A1C to Time-in-Range in Patients with Diabetes. Diabetes Technology & Therapeutics, 21(2), 81–85. https://doi.org/10.1089/dia.2018.0310 Vounzoulaki, E., Khunti, K., Abner, S. C., Tan, B. K., Davies, M. J., & Gillies, C. L. (2020). Progression to type 2 diabetes in women with a known history of gestational diabetes: Systematic review and meta-analysis. BMJ, m1361. https://doi.org/10.1136/bmj.m1361 Wang, L., Wang, X., Chen, A., Jin, X., & Che, H. (2020). Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model. Healthcare, 8(3), Article 3. https://doi.org/10.3390/healthcare8030247 Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., Yu, H., Gong, W., Bai, Y., Li, L., Zhu, Y., Wang, L., & Tian, J. (2020). A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. European Respiratory Journal, 56(2). https://doi.org/10.1183/13993003.00775-2020 Wei, J.-N., Sung, F.-C., Li, C.-Y., Chang, C.-H., Lin, R.-S., Lin, C.-C., Chiang, C.-C., & Chuang, L.-M. (2003). Low birth weight and high birth weight infants are both at an increased risk to have type 2 diabetes among schoolchildren in taiwan. Diabetes Care, 26(2), 343–348. https://doi.org/10.2337/diacare.26.2.343 White, N. H., Sun, W., Cleary, P. A., Tamborlane, W. V., Danis, R. P., Hainsworth, D. P., & Davis, M. D. (2010). Effect of Prior Intensive Therapy in Type 1 Diabetes on 10-Year Progression of Retinopathy in the DCCT/EDIC: Comparison of Adults and Adolescents. Diabetes, 59(5), 1244–1253. https://doi.org/10.2337/db09-1216 Wittler, I., Liu, X., & Dong, A. (2019). Deep Learning Enabled Predicting Modeling of Mortality of Diabetes Mellitus Patients. Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), 1–6. https://doi.org/10.1145/3332186.3333262 World Health Organization. (2013). Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy (WHO/NMH/MND/13.2). World Health Organization. https://apps.who.int/iris/handle/10665/85975 Wu, Y., Zhang, Q., Hu, Y., Sun-Woo, K., Zhang, X., Zhu, H., jie, L., & Li, S. (2022). Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems. Future Generation Computer Systems, 129, 1–12. https://doi.org/10.1016/j.future.2021.11.003 Xu, B., Guo, X., Ye, Y., & Cheng, J. (2012). An Improved Random Forest Classifier for Text Categorization. Journal of Computers, 7(12), 2913–2920. https://doi.org/10.4304/jcp.7.12.2913-2920 Yamada, T., Iwasaki, K., Maedera, S., Ito, K., Takeshima, T., Noma, H., & Shojima, N. (2020). Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: Proportional hazards analysis by deep neural network based machine learning. Current Medical Research and Opinion, 36(3), 403–409. https://doi.org/10.1080/03007995.2019.1706043 Younes, N., Atallah, M., Alam, R., Chehade, N., & Gannagé-Yared, M.-H. (2019). HbA1c and Blood Pressure Measurements: Relation with Gender, Body Mass Index, Study Field, and Lifestyle in Lebanese Students. Endocrine Practice, 25. https://doi.org/10.4158/EP-2019-0163 Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019). PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere, 10(7), Article 7. https://doi.org/10.3390/atmos10070373 Zeng, X., Chen, Y.-W., & Tao, C. (2009). Feature Selection Using Recursive Feature Elimination for Handwritten Digit Recognition. 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 1205–1208. https://doi.org/10.1109/IIH-MSP.2009.145 Zhou, B., Lu, Y., Hajifathalian, K., Bentham, J., Cesare, M. D., Danaei, G., Bixby, H., Cowan, M. J., Ali, M. K., Taddei, C., Lo, W. C., Reis-Santos, B., Stevens, G. A., Riley, L. M., Miranda, J. J., Bjerregaard, P., Rivera, J. A., Fouad, H. M., Ma, G., … Cisneros, J. Z. (2016). Worldwide trends in diabetes since 1980: A pooled analysis of 751 population-based studies with 4·4 million participants. The Lancet, 387(10027), 1513–1530. https://doi.org/10.1016/S0140-6736(16)00618-8 Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2021). Deep Learning for Diabetes: A Systematic Review. IEEE Journal of Biomedical and Health Informatics, 25(7), 2744–2757. https://doi.org/10.1109/JBHI.2020.3040225 Zuberi, Z., Sauli, E., Cun, L., Deng, J., Li, W.-J., He, X.-L., & Li, W. (2020). Insulin-delivery methods for children and adolescents with type 1 diabetes. Therapeutic Advances in Endocrinology and Metabolism, 11, 2042018820906016. https://doi.org/10.1177/2042018820906016 中華民國糖尿病衛教協會. (2019). 臺灣糖尿病年鑑2019第2型糖尿病. 伍昀貞. (2017). 利用傾向分數配對法重新評估苗栗縣卓蘭鎮衛生所糖尿病共同照護網病患成效分析 [碩士論文, 亞洲大學]. 亞洲大學健康產業管理學系長期照護組碩士在職專班. https://hdl.handle.net/11296/7485p9 劉棻. (2006). 台灣糖尿病共同照護網推行現況與挑戰. 領導護理, 7(2), 28–34. https://doi.org/10.29494/LN.200612.0003 吳亭亭. (2012). 糖尿病病人參與糖尿病共同照護網成效之評估 [碩士論文, 臺北醫學大學]. 臺北醫學大學醫務管理學研究所. https://hdl.handle.net/11296/s6nnjg 朱薇蓁. (2020). 糖尿病共照網病患糖尿病控制與健康促進行為相關性之研究-以桃園市八德區衛生所為例 [碩士論文, 國立體育大學]. 國立體育大學管理學院. https://hdl.handle.net/11296/wgq3td 李洺樺. (2019). 醫師供給偏離程度與糖尿病照護品質相關性之探討 [碩士論文, 長庚大學]. 長庚大學醫務管理學系. https://hdl.handle.net/11296/74pq5a 林佩珍. (2021). 糖尿病共同照護網個案健康識能與糖尿病控制成效之分析-以金門縣某鄉鎮為例 [碩士論文, 國立金門大學]. 國立金門大學管理學院事業經營碩士在職專班觀光管理組. https://hdl.handle.net/11296/289vm9 社團法人中華民國糖尿病學會. (2022a). 2022 第 1 型糖尿病臨床照護指引. 社團法人中華民國糖尿病學會. 社團法人中華民國糖尿病學會. (2022b). 2022 第 2 型糖尿病臨床照護指引. 社團法人中華民國糖尿病學會. 衛生福利部中央健康保險署. (2022). 糖尿病及初期慢性腎臟病照護整合方案. 統計處. https://www.tsn.org.tw/archive/20220330/ee45d783-07df-4c3c-8f49-358ac94b1e3a/ee45d783-07df-4c3c-8f49-358ac94b1e3a.pdf 衛生福利部中央健康保險署. (2023). 2021年國人全民健康保險就醫疾病資訊. 衛生福利部中央健康保險署; 衛生福利部中央健康保險署. https://www.nhi.gov.tw/Content_List.aspx?n=DEA170E82BF98015&topn=23C660CAACAA159D&Create=1 衛生福利部全民健康保險會. (2022). 全民健康保險醫療給付費用總額協商參考指標要覽-111年版統計處. https://www.tsn.org.tw/archive/20220330/ee45d783-07df-4c3c-8f49-358ac94b1e3a/ee45d783-07df-4c3c-8f49-358ac94b1e3a.pdf 衛生福利部國民健康署. (2017). 糖尿病共同照護工作指引手冊 (第一版). 衛生福利部國民健康署. https://www.govbooks.com.tw/books/115606 衛生福利部國民健康署. (2019). 衛生福利部國民健康署：糖尿病防治手冊（糖尿病預防、診斷與控制流程指引）- 醫事人員參考 (涵蓋範圍) 衛生福利部國民健康署; 衛生福利部國民健康署. https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=359&pid=1235 衛生福利部統計處. (2021). 歷年統計. 統計處; 統計處. https://dep.mohw.gov.tw/DOS/lp-5069-113.html 陳睿俊. (2009). 糖尿病病患加入糖尿病照護網滿意度及相關因素之探討－以龍潭地區為例 [碩士論文, 中華大學]. 中華大學科技管理學系(所). https://hdl.handle.net/11296/bmz9h5 陳羿伶. (2009). 糖尿病照護網病人流失其相關因素之探討 [碩士論文, 臺北醫學大學]. 臺北醫學大學醫務管理學研究所. https://hdl.handle.net/11296/4jguv2
指導教授	曾筱珽蔡邦維	審核日期	2023-7-20
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