Diabetes Metab J.  2024 Sep;48(5):1008-1011. 10.4093/dmj.2024.0490.

Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9)

Affiliations
  • 1Department of Endocrinology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
  • 2International School of Nursing, Hainan Medical University, Haikou, China
  • 3The First Affiliated Hospital of Hainan Medical University, Hainan Clinical Research Center for Metabolic Disease, Haikou, China


Figure

  • Fig. 1. Mini program of diabetic kidney disease (DKD) risk prediction model. BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.


Reference

1. Yun C, Tang F, Gao Z, Wang W, Bai F, Miller JD, et al. Construction of risk prediction model of type 2 diabetic kidney disease based on deep learning. Diabetes Metab J. 2024; 48:771–9.
Article
2. Choi JW, Lee CH, Park JS. Comparison of laboratory indices of non-alcoholic fatty liver disease for the detection of incipient kidney dysfunction. PeerJ. 2019; 7:e6524.
Article
3. Choi JW, Kim TH, Park JS, Lee CH. Association between relative thrombocytosis and microalbuminuria in adults with mild fasting hyperglycemia. J Pers Med. 2024; 14:89.
Article
4. Albrecht T, Rossberg A, Albrecht JD, Nicolay JP, Straub BK, Gerber TS, et al. Deep learning-enabled diagnosis of liver adenocarcinoma. Gastroenterology. 2023; 165:1262–75.
Article
5. Levy J, Alvarez D, Del Campo F, Behar JA. Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry. Nat Commun. 2023; 14:4881.
Article
6. Placido D, Yuan B, Hjaltelin JX, Zheng C, Haue AD, Chmura PJ, et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med. 2023; 29:1113–22.
Article
7. Tian F, Liu D, Wei N, Fu Q, Sun L, Liu W, et al. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat Med. 2024; 30:1309–19.
Article
8. Liu XZ, Duan M, Huang HD, Zhang Y, Xiang TY, Niu WC, et al. Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study. Front Endocrinol (Lausanne). 2023; 14:1184190.
Article
9. Allen A, Iqbal Z, Green-Saxena A, Hurtado M, Hoffman J, Mao Q, et al. Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2022; 10:e002560.
Article
10. He M, Gao J, Liu W, Tang X, Tang S, Long Q. Case management of patients with type 2 diabetes mellitus: a cross-sectional survey in Chongqing, China. BMC Health Serv Res. 2017; 17:129.
Article
11. Xia Z, Luo X, Wang Y, Xu T, Dong J, Jiang W, et al. Diabetic kidney disease screening status and related factors: a cross-sectional study of patients with type 2 diabetes in six provinces in China. BMC Health Serv Res. 2024; 24:489.
Article
12. McLernon DJ, Giardiello D, Van Calster B, Wynants L, van Geloven N, van Smeden M, et al. Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models. Ann Intern Med. 2023; 176:105–14.
Article
13. Slieker RC, van der Heijden AAWA, Siddiqui MK, LangendoenGort M, Nijpels G, Herings R, et al. Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study. BMJ. 2021; 374:n2134.
Article
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