1. Sacks DB, Bruns DE, Goldstein DE, Maclaren NK, McDonald JM, Parrott M. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem. 2002; 48:436–72.
Article
2. American Diabetes Association. Standards of medical care in diabetes. Diabetes Care. 2004; 27 Suppl 1:S15–35.
3. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ; A1c-Derived Average Glucose Study Group. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008; 31:1473–8.
Article
4. Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, Rand L, Siebert C; Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993; 329:977–86.
Article
5. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998; 352:837–53.
6. Singer DE, Nathan DM, Anderson KM, Wilson PW, Evans JC. Association of HbA1c with prevalent cardiovascular disease in the original cohort of the Framingham Heart Study. Diabetes. 1992; 41:202–8.
Article
7. Chien KL, Lin HJ, Lee BC, Hsu HC, Chen MF. Prediction model for high glycated hemoglobin concentration among ethnic Chinese in Taiwan. Cardiovasc Diabetol. 2010; 9:59.
Article
8. Huang CL, Iqbal U, Nguyen PA, Chen ZF, Clinciu DL, Hsu YH, Hsu CH, Jian WS. Using hemoglobin A1C as a predicting model for time interval from pre-diabetes progressing to diabetes. PLoS One. 2014; 9:e104263.
Article
9. Rauh SP, Heymans MW, Koopman AD, Nijpels G, Stehouwer CD, Thorand B, Rathmann W, Meisinger C, Peters A, de Las Heras Gala T, Glümer C, Pedersen O, Cederberg H, Kuusisto J, Laakso M, Pearson ER, Franks PW, Rutters F, Dekker JM. Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study. PLoS One. 2017; 12:e0171816.
Article
10. Tianqi C, Guestrin C. Xgboost: a scalable tree boosting system. In : Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13-17; San Francisco, CA. New York, NY. Association for Computing Machinery. 2016. p. 785–94.
11. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In : Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4-9; Long Beach, CA. Red Hook, NY. Curran Associates Inc.2017. p. 4768–77.
12. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020; 2:56–67.
Article
13. Shavitt I, Segal E. Regularization learning networks: deep learning for tabular datasets. In : Proceedings of the 32nd International Conference on Neural Information Processing Systems 2018; 2018 Dec 3-8; Montréal, Canada. Red Hook, NY. Curran Associates Inc.2018. p. 1386–96.
15. Cai H, Zhong R, Wang C, Zhou R, Zhou K, Lee H, Xu K, Gao Z, Zhong R, Luo J, Zhou Y, Ding M, Li L, Li Q, Li D, Jiang N, Cheng X, Cui S, Ye H, Shen J. KDD CUP 2017 travel time prediction [Internet]. KDD;2017. [cited 2021 Sep 10]. Available from:
https://www.kdd.org/kdd2017/files/Task1_3rdPlace.pdf.
16. Kim HS, Kim DJ, Yoon KH. Medical big data is not yet available: why we need realism rather than exaggeration. Endocrinol Metab (Seoul). 2019; 34:349–54.
Article
17. Kim HS, Kim JH. Proceed with caution when using real world data and real world evidence. J Korean Med Sci. 2019; 34:e28.
Article
18. Hu ZD, Zhang KP, Huang Y, Zhu S. Compliance to self-monitoring of blood glucose among patients with type 2 diabetes mellitus and its influential factors: a real-world cross-sectional study based on the Tencent TDF-I blood glucose monitoring platform. mHealth. 2017; 3:25.
Article
19. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001; 29:1189–232.
Article
20. Yelin I, Snitser O, Novich G, Katz R, Tal O, Parizade M, Chodick G, Koren G, Shalev V, Kishony R. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat Med. 2019; 25:1143–52.
Article
21. DeCoste D, Wagstaff K. Alpha seeding for support vector machines. In : Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000 Aug 20-23; Boston, MA. New York, NY. Association for Computing Machinery. 2000. p. 345–9.
22. Kim HS, Lee S, Kim JH. Real-world evidence versus randomized controlled trial: clinical research based on electronic medical records. J Korean Med Sci. 2018; 33:e213.
Article
23. Landgraf R. The relationship of postprandial glucose to HbA1c. Diabetes Metab Res Rev. 2004; 20 Suppl 2:S9–12.
Article
24. Ketema EB, Kibret KT. Correlation of fasting and postprandial plasma glucose with HbA1c in assessing glycemic control; systematic review and meta-analysis. Arch Public Health. 2015; 73:43.
Article