188 related articles for article (PubMed ID: 36239780)
1. Development and validation of medical record-based logistic regression and machine learning models to diagnose diabetic retinopathy.
Li HY; Dong L; Zhou WD; Wu HT; Zhang RH; Li YT; Yu CY; Wei WB
Graefes Arch Clin Exp Ophthalmol; 2023 Mar; 261(3):681-689. PubMed ID: 36239780
[TBL] [Abstract][Full Text] [Related]
2. Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.
Wu JH; Liu TYA; Hsu WT; Ho JH; Lee CC
J Med Internet Res; 2021 Jul; 23(7):e23863. PubMed ID: 34407500
[TBL] [Abstract][Full Text] [Related]
3. Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.
He F; Ng Yin Ling C; Nusinovici S; Cheng CY; Wong TY; Li J; Sabanayagam C
J Med Internet Res; 2024 Mar; 26():e41065. PubMed ID: 38546730
[TBL] [Abstract][Full Text] [Related]
4. Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study.
Oh E; Yoo TK; Park EC
BMC Med Inform Decis Mak; 2013 Sep; 13():106. PubMed ID: 24033926
[TBL] [Abstract][Full Text] [Related]
5. Lower hydration status increased diabetic retinopathy among middle-aged adults and older adults: Results from NHANES 2005-2008.
Zhang J; Ren Z; Zhang Q; Zhang R; Zhang C; Liu J
Front Public Health; 2022; 10():1023747. PubMed ID: 36388275
[TBL] [Abstract][Full Text] [Related]
6. Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China.
Li W; Song Y; Chen K; Ying J; Zheng Z; Qiao S; Yang M; Zhang M; Zhang Y
BMJ Open; 2021 Nov; 11(11):e050989. PubMed ID: 34836899
[TBL] [Abstract][Full Text] [Related]
7. A systematic comparison of machine learning algorithms to develop and validate prediction model to predict heart failure risk in middle-aged and elderly patients with periodontitis (NHANES 2009 to 2014).
Wang Y; Xiao Y; Zhang Y
Medicine (Baltimore); 2023 Aug; 102(34):e34878. PubMed ID: 37653785
[TBL] [Abstract][Full Text] [Related]
8. Modifiable lifestyle, mental health status and diabetic retinopathy in U.S. adults aged 18-64 years with diabetes: a population-based cross-sectional study from NHANES 1999-2018.
Li B; Zhou C; Gu C; Cheng X; Wang Y; Li C; Ma M; Fan Y; Xu X; Chen H; Zheng Z
BMC Public Health; 2024 Jan; 24(1):11. PubMed ID: 38166981
[TBL] [Abstract][Full Text] [Related]
9. Automated detection of diabetic retinopathy using machine learning classifiers.
Alabdulwahhab KM; Sami W; Mehmood T; Meo SA; Alasbali TA; Alwadani FA
Eur Rev Med Pharmacol Sci; 2021 Jan; 25(2):583-590. PubMed ID: 33577010
[TBL] [Abstract][Full Text] [Related]
10. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women.
Shim JG; Kim DW; Ryu KH; Cho EA; Ahn JH; Kim JI; Lee SH
Arch Osteoporos; 2020 Oct; 15(1):169. PubMed ID: 33097976
[TBL] [Abstract][Full Text] [Related]
11. Glycaemic and haemoglobin A1c thresholds for detecting diabetic retinopathy: the fifth Korea National Health and Nutrition Examination Survey (2011).
Park YM; Ko SH; Lee JM; Kim DJ; Kim DJ; Han K; Bower JK; Ahn YB;
Diabetes Res Clin Pract; 2014 Jun; 104(3):435-42. PubMed ID: 24785739
[TBL] [Abstract][Full Text] [Related]
12. Association between Dietary Choline Intake and Diabetic Retinopathy: National Health and Nutrition Examination Survey 2005-2008.
Liu W; Ren C; Zhang W; Liu G; Lu P
Curr Eye Res; 2022 Feb; 47(2):269-276. PubMed ID: 34328805
[TBL] [Abstract][Full Text] [Related]
13. Identifying the severity of diabetic retinopathy by visual function measures using both traditional statistical methods and interpretable machine learning: a cross-sectional study.
Wright DM; Chakravarthy U; Das R; Graham KW; Naskas TT; Perais J; Kee F; Peto T; Hogg RE
Diabetologia; 2023 Dec; 66(12):2250-2260. PubMed ID: 37725107
[TBL] [Abstract][Full Text] [Related]
14. Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.
Gui Y; Gui S; Wang X; Li Y; Xu Y; Zhang J
Sci Rep; 2024 Jun; 14(1):13049. PubMed ID: 38844504
[TBL] [Abstract][Full Text] [Related]
15. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
Ting DSW; Cheung CY; Lim G; Tan GSW; Quang ND; Gan A; Hamzah H; Garcia-Franco R; San Yeo IY; Lee SY; Wong EYM; Sabanayagam C; Baskaran M; Ibrahim F; Tan NC; Finkelstein EA; Lamoureux EL; Wong IY; Bressler NM; Sivaprasad S; Varma R; Jonas JB; He MG; Cheng CY; Cheung GCM; Aung T; Hsu W; Lee ML; Wong TY
JAMA; 2017 Dec; 318(22):2211-2223. PubMed ID: 29234807
[TBL] [Abstract][Full Text] [Related]
16. Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults.
Lynam AL; Dennis JM; Owen KR; Oram RA; Jones AG; Shields BM; Ferrat LA
Diagn Progn Res; 2020; 4():6. PubMed ID: 32607451
[TBL] [Abstract][Full Text] [Related]
17. Associations between psycho-behavioral risk factors and diabetic retinopathy: NHANES (2005-2018).
Sun XJ; Zhang GH; Guo CM; Zhou ZY; Niu YL; Wang L; Dou GR
Front Public Health; 2022; 10():966714. PubMed ID: 36187629
[TBL] [Abstract][Full Text] [Related]
18. Predicting the risk of diabetic retinopathy using explainable machine learning algorithms.
Islam MM; Rahman MJ; Rabby MS; Alam MJ; Pollob SMAI; Ahmed NAMF; Tawabunnahar M; Roy DC; Shin J; Maniruzzaman M
Diabetes Metab Syndr; 2023 Dec; 17(12):102919. PubMed ID: 38091881
[TBL] [Abstract][Full Text] [Related]
19. Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.
Tsao HY; Chan PY; Su EC
BMC Bioinformatics; 2018 Aug; 19(Suppl 9):283. PubMed ID: 30367589
[TBL] [Abstract][Full Text] [Related]
20. Depression in Individuals With Diabetic Retinopathy in the US National Health and Nutrition Examination Survey, 2005-2008.
Valluru G; Costa A; Klawe J; Liu B; Deobhakta A; Ahmad S
Am J Ophthalmol; 2023 Dec; 256():63-69. PubMed ID: 37495007
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]