參考文獻 |
AbuAlhommos, A. K., Alturaifi, A. H., Hamdhah, A. M. A.-B., Al-Ramadhan, H. H., Ali, Z. A. A., & Nasser, H. J. A. (2022). The Health-Related Quality of Life of Patients with Type 2 Diabetes in Saudi Arabia. Patient Preference and Adherence, 16, 1233–1245. https://doi.org/10.2147/PPA.S353525
Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2020). Chapter 9—Calibration of CGM systems. In C. Fabris & B. Kovatchev (Eds.), Glucose Moni-toring Devices (pp. 173–201). Academic Press. https://doi.org/10.1016/B978-0-12-816714-4.00009-0
Acciaroli, G., Vettoretti, M., Facchinetti, A., Sparacino, G., & Cobelli, C. (2018). Reduc-tion of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework. IEEE Transactions on Biomedical Engineering, 65(3), 587–595. https://doi.org/10.1109/TBME.2017.2706974
Aleppo, G., Ruedy, K. J., Riddlesworth, T. D., Kruger, D. F., Peters, A. L., Hirsch, I., Bergenstal, R. M., Toschi, E., Ahmann, A. J., Shah, V. N., Rickels, M. R., Bode, B. W., Philis-Tsimikas, A., Pop-Busui, R., Rodriguez, H., Eyth, E., Bhargava, A., Kollman, C., & Beck, R. W. (2017). REPLACE-BG: A Randomized Trial Com-paring Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Well-Controlled Type 1 Diabetes. Diabetes Care, 40(4), 538–545. https://doi.org/10.2337/dc16-2482
Alsharif, A., Wong, I., Ma, T., Lau, W., Alhamed, M., Alwafi, H., & Wei, li. (2023). The association between dementia and the risk of hypoglycaemia events among patients with diabetes mellitus: A propensity-score matched cohort analysis. Fron-tiers in Medicine, 10. https://doi.org/10.3389/fmed.2023.1177636
Al-Taee, A. M., Al-Taee, M. A., Al-Nuaimy, W., Muhsin, Z. J., & AlZu’bi, H. (2015). Smart Bolus Estimation Taking into Account the Amount of Insulin on Board. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Se-cure Computing; Pervasive Intelligence and Computing, 1051–1056. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.358
Atkinson, M. A., Eisenbarth, G. S., & Michels, A. W. (2014). Type 1 diabetes. The Lan-cet, 383(9911), 69–82. https://doi.org/10.1016/S0140-6736(13)60591-7
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., Cobelli, C., Dassau, E., DeVries, J. H., Donaghue, K. C., Dovc, K., Doyle, F. J., III, Garg, S., Grunberger, G., Heller, S., … Phillip, M. (2019). Clinical Targets for Continuous Glucose Moni-toring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care, 42(8), 1593–1603. https://doi.org/10.2337/dci19-0028
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017). The Fallacy of Average: How Using HbA1c Alone to Assess Glycemic Control Can Be Misleading. Diabetes Care, 40(8), 994–999. https://doi.org/10.2337/dc17-0636
Bergenstal, R. M., Beck, R. W., Close, K. L., Grunberger, G., Sacks, D. B., Kowalski, A., Brown, A. S., Heinemann, L., Aleppo, G., Ryan, D. B., Riddlesworth, T. D., & Cefalu, W. T. (2018). Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care, 41(11), 2275–2280. https://doi.org/10.2337/dc18-1581
Bhattacharya, O., Alva, R., K.p, P., Reddy, G. V. A., & Kotian, H. (2023). Diabetes Mellitus and quality of life: Lower socio-economic status patients in Indian ter-tiary healthcare – a cross sectional study (12:591). F1000Research. https://doi.org/10.12688/f1000research.130584.1
Camerlingo, N., Vettoretti, M., Del Favero, S., Facchinetti, A., Choudhary, P., & Spara-cino, G. (2022). Generation of post-meal insulin correction boluses in type 1 dia-betes simulation models for in-silico clinical trials: More realistic scenarios ob-tained using a decision tree approach. Computer Methods and Programs in Bio-medicine, 221, 106862. https://doi.org/10.1016/j.cmpb.2022.106862
Chan, N. B., Li, W., Aung, T., Bazuaye, E., & Montero, R. M. (2023a). Machine Learn-ing–Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study. JMIR AI, 2(1), e45450. https://doi.org/10.2196/45450
Chan, N. B., Li, W., Aung, T., Bazuaye, E., & Montero, R. M. (2023b). Time in patterns: Machine learning based blood glucose fluctuation pattern recognition for Type 1 diabetes management in continuous glucose monitoring. JMIR AI, 2023(2), Article 2. https://centaur.reading.ac.uk/111564/
Chehregosha, H., Khamseh, M. E., Malek, M., Hosseinpanah, F., & Ismail-Beigi, F. (2019). A View Beyond HbA1c: Role of Continuous Glucose Monitoring. Dia-betes Therapy, 10(3), 853–863. https://doi.org/10.1007/s13300-019-0619-1
Cnop, M., Toivonen, S., Igoillo-Esteve, M., & Salpea, P. (2017). Endoplasmic reticulum stress and eIF2α phosphorylation: The Achilles heel of pancreatic β cells. Molecu-lar Metabolism, 6(9), 1024–1039. https://doi.org/10.1016/j.molmet.2017.06.001
Dulyapach, K., Ngamchaliew, P., Vichitkunakorn, P., Sornsenee, P., & Choomalee, K. (2022). Prevalence and Associated Factors of Delayed Diagnosis of Type 2 Dia-betes Mellitus in a Tertiary Hospital: A Retrospective Cohort Study. International Journal of Public Health, 67. https://doi.org/10.3389/ijph.2022.1605039
ElSayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., Collins, B. S., Gaglia, J. L., 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., American Diabetes Association. (2022). Classification and Diagnosis of Di-abetes: 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 diabe-tes mellitus: Towards a continuum of diabetes subtypes. Nature Reviews. Endo-crinology, 12(7), 394–406. https://doi.org/10.1038/nrendo.2016.50
Giammarino, F., Senanayake, R., Prahalad, P., Maahs, D. M., & Scheinker, D. (2024). A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Con-tinuous Glucose Monitoring Data. Journal of Diabetes Science and Technology, 19322968241236208. https://doi.org/10.1177/19322968241236208
Gregg, E. W., Li, Y., Wang, J., Burrows, N. R., Ali, M. K., Rolka, D., Williams, D. E., & Geiss, L. (2014). Changes in diabetes-related complications in the United States, 1990-2010. The New England Journal of Medicine, 370(16), 1514–1523. https://doi.org/10.1056/NEJMoa1310799
Guerra, S., Facchinetti, A., Sparacino, G., Nicolao, G. D., & Cobelli, C. (2012). Enhanc-ing the Accuracy of Subcutaneous Glucose Sensors: A Real-Time Deconvolution-Based Approach. IEEE Transactions on Biomedical Engineering, 59(6), 1658–1669. https://doi.org/10.1109/TBME.2012.2191782
Haraldstad, K., Wahl, A., Andenæs, R., Andersen, J. R., Andersen, M. H., Beisland, E., Borge, C. R., Engebretsen, E., Eisemann, M., Halvorsrud, L., Hanssen, T. A., Haugstvedt, A., Haugland, T., Johansen, V. A., Larsen, M. H., Løvereide, L., Løyland, B., Kvarme, L. G., Moons, P., … LIVSFORSK network. (2019). A systematic review of quality of life research in medicine and health sciences. Qual-ity of Life Research: An International Journal of Quality of Life Aspects of Treat-ment, Care and Rehabilitation, 28(10), 2641–2650. https://doi.org/10.1007/s11136-019-02214-9
Hattersley, A. T., Greeley, S. A. W., Polak, M., Rubio-Cabezas, O., Njølstad, P. R., Mlynarski, W., Castano, L., Carlsson, A., Raile, K., Chi, D. V., Ellard, S., & Craig, M. E. (2018). ISPAD Clinical Practice Consensus Guidelines 2018: The diagnosis and management of monogenic diabetes in children and adolescents. Pediatric Diabetes, 19 Suppl 27, 47–63. https://doi.org/10.1111/pedi.12772
International Classification of Diseases, Eleventh Revision (ICD-11). (2019). World Health Organization (WHO). https://icd.who.int/browse11. Licensed under Crea-tive Commons Attribution-NoDerivatives 3.0 IGO licence (CC BY-ND 3.0 IGO)
Jabeen, M. (2020). Insulin treatment for diabetes. InnovAiT, 13(12), 739–746. https://doi.org/10.1177/1755738020958752
Khan, S. G., & Huda, M. S. (2017). Hypoglycemia and Cardiac Arrhythmia; Mecha-nisms, Evidence Base a nd Current Recommendations. Current Diabetes Reviews, 13(6), 590–597. https://doi.org/10.2174/1573399812666161201155941
Klonoff, D. C., Wang, J., Rodbard, D., Kohn, M. A., Li, C., Liepmann, D., Kerr, D., Ahn, D., Peters, A. L., Umpierrez, G. E., Seley, J. J., Xu, N. Y., Nguyen, K. T., Si-monson, G., Agus, M. S. D., Al-Sofiani, M. E., Armaiz-Pena, G., Bailey, T. S., Basu, A., … Kovatchev, B. (2023). A Glycemia Risk Index (GRI) of Hypogly-cemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Cli-nician Ratings. Journal of Diabetes Science and Technology, 17(5), 1226–1242. https://doi.org/10.1177/19322968221085273
Lin, L., Chen, Z., Huang, C., Wu, Y., Huang, L., Wang, L., Ke, S., & Liu, L. (2023). Mi-to-TEMPO, a Mitochondria-Targeted Antioxidant, Improves Cognitive Dysfunc-tion due to Hypoglycemia: An Association with Reduced Pericyte Loss and Blood-Brain Barrier Leakage. Molecular Neurobiology, 60(2), 672–686. https://doi.org/10.1007/s12035-022-03101-0
Maiorino, M. I., Petrizzo, M., Bellastella, G., & Esposito, K. (2018). Continuous glucose monitoring for patients with type 1 diabetes on multiple daily injections of insulin: Pros and cons. Endocrine, 59(1), 62–65. https://doi.org/10.1007/s12020-017-1328-z
McCrimmon, R. J., & Frier, B. M. (1994). Hypoglycaemia, the most feared complication of insulin therapy. Diabete & Metabolisme, 20(6), 503–512.
McCrimmon, R. J., Frier, B. M., & Deary, I. J. (1999). Appraisal of Mood and Person-ality During Hypoglycaemia in Human Subjects. Physiology & Behavior, 67(1), 27–33. https://doi.org/10.1016/S0031-9384(99)00035-9
McCrimmon, R. J., & Sherwin, R. S. (2010). Hypoglycemia in Type 1 Diabetes. Diabe-tes, 59(10), 2333–2339. https://doi.org/10.2337/db10-0103
Mohanty, S., & Saini, S. K. (2022). Diabetes, A Culprit Against Quality of Life? Nation-al Journal of Community Medicine, 13(12), Article 12. https://doi.org/10.55489/njcm.131220222462
Munoz-Organero, M. (2020). Deep Physiological Model for Blood Glucose Prediction in T1DM Patients. Sensors, 20(14), Article 14. https://doi.org/10.3390/s20143896
Mwadulo, D. W., Madhavi, M., & Nkoroi, B. (2023). Assessment of health-related quality of life in type 2 diabetes mellitus at Moi County referral hospital, Taita Taveta county (p. 2023.01.31.23285237). medRxiv. https://doi.org/10.1101/2023.01.31.23285237
Nagaraj, S. B., Sidorenkov, G., van Boven, J. F. M., & Denig, P. (2019). Predicting short- and long-term glycated haemoglobin response after insulin initiation in pa-tients with type 2 diabetes mellitus using machine-learning algorithms. Diabetes, Obesity and Metabolism, 21(12), 2704–2711. https://doi.org/10.1111/dom.13860
Nimri, R., Battelino, T., Laffel, L. M., Slover, R. H., Schatz, D., Weinzimer, S. A., Dovc, K., Danne, T., & Phillip, M. (2020). Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nature Medicine, 26(9), Article 9. https://doi.org/10.1038/s41591-020-1045-7
Noaro, G., Cappon, G., Sparacino, G., Del Favero, S., & Facchinetti, A. (2020). Nonlin-ear Machine Learning Models for Insulin Bolus Estimation in Type 1 Diabetes Therapy. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 5502–5505. https://doi.org/10.1109/EMBC44109.2020.9176021
Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2021). Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy. IEEE Transactions on Biomedical Engineering, 68(1), 247–255. https://doi.org/10.1109/TBME.2020.3004031
Perkins, B. A., Sherr, J. L., & Mathieu, C. (2021). Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation. Science (New York, N.Y.), 373(6554), 522–527. https://doi.org/10.1126/science.abg4502
Pham, T. M., Carpenter, J. R., Morris, T. P., Sharma, M., & Petersen, I. (2019). Ethnic Differences in the Prevalence of Type 2 Diabetes Diagnoses in the UK: Cross-Sectional Analysis of the Health Improvement Network Primary Care Database. Clinical Epidemiology, 11, 1081–1088. https://doi.org/10.2147/CLEP.S227621
Przezak, A., Bielka, W., & Molęda, P. (2022). Fear of hypoglycemia—An underestimat-ed problem. Brain and Behavior, 12(7), e2633. https://doi.org/10.1002/brb3.2633
Quintanilla Rodriguez, B. S., & Mahdy, H. (2024). Gestational Diabetes. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK545196/
Rahman, R., Almomen, F., Artam Alajmi, A., Asir, I., Basudan, S., Alenezi, M., Alabdulwahab, F., shammari, S., Aldakheel, A., Shehri, A., & Alabdulmohsen, M. (2022). Predictors and Associated Risk Factors of Development of Type 2 Di-abetes Mellitus. JOURNAL OF HEALTHCARE SCIENCES, 02, 100–105. https://doi.org/10.52533/JOHS.2022.2603
Song, C., Lyu, Y., Li, C., Liu, P., Li, J., Ma, R. C., & Yang, X. (2018). Long-term risk of diabetes in women at varying durations after gestational diabetes: A systematic review and meta-analysis with more than 2 million women. Obesity Reviews: An Official Journal of the International Association for the Study of Obesity, 19(3), 421–429. https://doi.org/10.1111/obr.12645
Syafrudin, M., Alfian, G., Fitriyani, N. L., Fahrurrozi, I., Anshari, M., & Rhee, J. (2022). A Personalized Blood Glucose Prediction Model Using Random Forest Regres-sion. 2022 ASU International Conference in Emerging Technologies for Sustain-ability and Intelligent Systems (ICETSIS), 295–299. https://doi.org/10.1109/ICETSIS55481.2022.9888838
Thapar, R., R, M., Gatty, N., Hegde, K., Unnikrishnan, B., Mithra, P., Holla, R., Suma, B., Rao, A., Nikitha, P., & M, A. (2023). Obstacles for self-management practices among diabetes patients: A facility-based study from Coastal South India. F1000Research, 12, 839. https://doi.org/10.12688/f1000research.138146.1
TOSCHI, E., ADAM, A., HURLBERT, R., FRIMPONG, N., SLYNE, C., LAFFEL, L. M., & MUNSHI, M. (2023). 361-OR: Hybrid Care Model Combining Telemedi-cine and Office Visits for Diabetes Management Is Effective in Older Adults with Type 1 Diabetes (T1D) Using Continuous Glucose Monitors (CGM). Diabetes, 72(Supplement_1), 361-OR. https://doi.org/10.2337/db23-361-OR
Tosur, M., & Redondo, M. J. (2018). Heterogeneity of Type 1 Diabetes: The Effect of Ethnicity. Current Diabetes Reviews, 14(3), 266–272. https://doi.org/10.2174/1573399813666170502105402
Townsend, C., Seron, M. M., & Magdelaine, N. (2022). Optimal Responses to Con-strained Bolus Inputs to Models of T1D. IFAC-PapersOnLine, 55(16), 190–195. https://doi.org/10.1016/j.ifacol.2022.09.022
Tsichlaki, S., Koumakis, L., & Tsiknakis, M. (2022). Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review. JMIR Diabetes, 7(3), e34699. https://doi.org/10.2196/34699
Vigersky, R. A., & Shin, J. (2024). The Myth of MARD (Mean Absolute Relative Dif-ference): Limitations of MARD in the Clinical Assessment of Continuous Glu-cose Monitoring Data. Diabetes Technology & Therapeutics, 26(S3), 38–44. https://doi.org/10.1089/dia.2023.0435
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., Chen, Y., Cui, Z., Lin, L., & Zong, Y. (2024). Diabetes Risk Analysis based on Machine Learning LASSO Regression Model. Journal of Theory and Practice of Engineering Science, 4(01), Article 01. https://doi.org/10.53469/jtpes.2024.04(01).08
Wilson, V. (2023). An overview of complications associated with type 1 and type 2 dia-betes. Nursing Standard (Royal College of Nursing (Great Britain): 1987), 38(7), 77–82. https://doi.org/10.7748/ns.2023.e11933
World Health Organization. (2019). Classification of diabetes mellitus. World Health Or-ganization; WHO IRIS. https://iris.who.int/handle/10665/325182
Wright, J. J., Hu, J.-R., Shajani-Yi, Z., & Bao, S. (2019). USE OF CONTINUOUS GLUCOSE MONITORING LEADS TO DIAGNOSIS OF HEMOGLOBIN C TRAIT IN A PATIENT WITH DISCREPANT HEMOGLOBIN A1C AND SELF-MONITORED BLOOD GLUCOSE. AACE Clinical Case Reports, 5(1), e31–e34. https://doi.org/10.4158/ACCR-2018-0149
Yan, Z., Cai, M., Han, X., Chen, Q., & Lu, H. (2023). The Interaction Between Age and Risk Factors for Diabetes and Prediabetes: A Community-Based Cross-Sectional Study. Diabetes, Metabolic Syndrome and Obesity, 16, 85–93. https://doi.org/10.2147/DMSO.S390857
Zeru, M. A., Tesfa, E., Mitiku, A. A., Seyoum, A., & Bokoro, T. A. (2021). Prevalence and risk factors of type-2 diabetes mellitus in Ethiopia: Systematic review and me-ta-analysis. Scientific Reports, 11(1), 21733. https://doi.org/10.1038/s41598-021-01256-9
Zhu, T., Kuang, L., Li, K., Zeng, J., Herrero, P., & Georgiou, P. (2021). Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge. 2021 IEEE In-ternational Symposium on Circuits and Systems (ISCAS), 1–5. https://doi.org/10.1109/ISCAS51556.2021.9401083
Zhu, T., Li, K., Chen, J., Herrero, P., & Georgiou, P. (2020). Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes. Journal of Healthcare In-formatics Research, 4(3), 308–324. https://doi.org/10.1007/s41666-020-00068-2 |