References
- Wilkinson-Berka JL , Miller AG . Update on the treatment of diabetic retinopathy. Sci World J . 2008;8:98–120. doi:10.1100/tsw.2008.25
- Deng YX , Ye WQ , Sun YT , Zhou ZY , Liang YB . [A meta-analysis of prevalence of diabetic retinopathy in China]. Zhonghua Yi Xue Za Zhi . 2020;100(48):3846–3852. Chinese.33371629
- Hu H , Sawhney M , Shi L , et al. A systematic review of the direct economic burden of type 2 diabetes in china. Diabetes Ther . 2015;6(1):7–16. doi:10.1007/s13300-015-0096-0 25652613
- Hunt MS , Kihara Y , Lee AY . Novel low-shot deep learning approach for retinal image classification with few examples. JAMA Ophthalmol . 2020;138(10):1077–1078. doi:10.1001/jamaophthalmol.2020.3256 32880607
- Yao L , Zhong Y , Wu J , et al. Multivariable logistic regression and back propagation artificial neural network to predict diabetic retinopathy. Diabetes Metab Syndr Obes . 2019;12:1943–1951. doi:10.2147/DMSO.S219842 31576158
- Wang J , Wang F , Liu Y , et al. Multiple linear regression and artificial neural network to predict blood glucose in overweight patients. Exp Clin Endocrinol Diabetes . 2016;124(1):34–38. doi:10.1055/s-0035-1565175 26797861
- Patel CJ , Pho N , McDuffie M , et al. A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data . 2016;3:160096. doi:10.1038/sdata.2016.96 27779619
- Gao F , Huang XL , Jiang XP , et al. Independent effect of alanine transaminase on the incidence of type 2 diabetes mellitus, stratified by age and gender: a secondary analysis based on a large cohort study in China. Clin Chim Acta . 2019;495:54–59. doi:10.1016/j.cca.2019.03.1636 30946812
- Mostofsky E , Chahal HS , Mukamal KJ , Rimm EB , Mittleman MA . Alcohol and immediate risk of cardiovascular events: a systematic review and dose-response meta-analysis. Circulation . 2016;133(10):979–987. doi:10.1161/CIRCULATIONAHA.115.019743 26936862
- Solomon SD , Goldberg MF . ETDRS grading of diabetic retinopathy: still the gold standard. Ophthalmic Res . 2019;62(4):190–195. doi:10.1159/000501372 31454808
- Xu JF , Xu J , Li SZ , et al. Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. PLoS Negl Trop Dis . 2013;7(3):e2123. doi:10.1371/journal.pntd.0002123 23556015
- Dai L , Fang R , Li H , et al. Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans Med Imaging . 2018;37(5):1149–1161. doi:10.1109/TMI.2018.2794988 29727278
- Johnsson J , Björnsson O , Andersson P , et al. Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care. Crit Care . 2020;24(1):474. doi:10.1186/s13054-020-03103-1 32731878
- Weng SF , Reps J , Kai J , Garibaldi JM , Qureshi N . Can machine-learning improve cardiovascular risk prediction using routine clinical data. PLoS One . 2017;12(4):e0174944. doi:10.1371/journal.pone.0174944 28376093
- Deo RC . Machine learning in medicine. Circulation . 2015;132(20):1920–1930. doi:10.1161/CIRCULATIONAHA.115.001593 26572668
- Tsao HY , Chan PY , Su EC . Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms. BMC Bioinform . 2018;19(Suppl 9):283. doi:10.1186/s12859-018-2277-0
- UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ . 1998;317(7160):703–713. doi:10.1136/bmj.317.7160.703 9732337
- Yau JW , Rogers SL , Kawasaki R , et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care . 2012;35(3):556–564. doi:10.2337/dc11-1909 22301125
- Association of diabetic retinopathy and diabetic macular edema with renal function in southern Chinese patients with type 2 diabetes mellitus: a single-center observational study, Dryad, Dataset, Available from: https://datadryad.org/stash/dataset/doi:10.5061/dryad.6kg1sd7.