J Korean Med Sci.
2024 Feb;39(5):e69. 10.3346/jkms.2024.39.e69.
Validation of Clinical Risk Model to Predict Future Diabetes
- Affiliations
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- 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Hospital, Busan, Korea
- 2Biomedical Research Institute, Pusan National University Hospital, Busan, Korea
- 3Department of Internal Medicine, Pusan National University School of Medicine, Yangsan, Korea
- 4Department of Biostatistics, Clinical Trial Center, Biomedical Research Institute, Pusan National University Hospital, Busan, Korea
- 5Department of Mathematics, Howard University, Washington, D.C., USA
- KMID: 2551985
- DOI: http://doi.org/10.3346/jkms.2024.39.e69
Reference
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1. GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023; 402(10397):203–234. PMID: 37356446.2. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007; 167(10):1068–1074. PMID: 17533210.3. Patro KK, Allam JP, Sanapala U, Marpu CK, Samee NA, Alabdulhafith M, et al. An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques. BMC Bioinformatics. 2023; 24(1):372. PMID: 37784049.4. Lee HA, Park H, Hong YS. Validation of the framingham diabetes risk model using community-based KoGES data. J Korean Med Sci. 2024; 39(5):e47.5. Peddinti G, Bergman M, Tuomi T, Groop L. 1-Hour Post-OGTT glucose improves the early prediction of type 2 diabetes by clinical and metabolic markers. J Clin Endocrinol Metab. 2019; 104(4):1131–1140. PMID: 30445509.
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