141 related articles for article (PubMed ID: 38091881)
1. 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]
2. 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]
3. A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.
Pan H; Sun J; Luo X; Ai H; Zeng J; Shi R; Zhang A
Front Med (Lausanne); 2023; 10():1136653. PubMed ID: 37181375
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia.
Islam MM; Alam MJ; Maniruzzaman M; Ahmed NAMF; Ali MS; Rahman MJ; Roy DC
PLoS One; 2023; 18(8):e0289613. PubMed ID: 37616271
[TBL] [Abstract][Full Text] [Related]
7. Using Machine Learning Techniques to Develop Risk Prediction Models for the Risk of Incident Diabetic Retinopathy Among Patients With Type 2 Diabetes Mellitus: A Cohort Study.
Zhao Y; Li X; Li S; Dong M; Yu H; Zhang M; Chen W; Li P; Yu Q; Liu X; Gao Z
Front Endocrinol (Lausanne); 2022; 13():876559. PubMed ID: 35655800
[TBL] [Abstract][Full Text] [Related]
8. Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics.
Yagin FH; Yasar S; Gormez Y; Yagin B; Pinar A; Alkhateeb A; Ardigò LP
Metabolites; 2023 Dec; 13(12):. PubMed ID: 38132885
[TBL] [Abstract][Full Text] [Related]
9. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
Gayathri S; Gopi VP; Palanisamy P
Phys Eng Sci Med; 2021 Sep; 44(3):639-653. PubMed ID: 34033015
[TBL] [Abstract][Full Text] [Related]
10. A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes.
Zong GW; Wang WY; Zheng J; Zhang W; Luo WM; Fang ZZ; Zhang Q
J Diabetes Res; 2023; 2023():3990035. PubMed ID: 37229505
[TBL] [Abstract][Full Text] [Related]
11. Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.
Yin JM; Li Y; Xue JT; Zong GW; Fang ZZ; Zou L
J Diabetes Res; 2024; 2024():8857453. PubMed ID: 38282659
[TBL] [Abstract][Full Text] [Related]
12. Study on risk factors of diabetic peripheral neuropathy and establishment of a prediction model by machine learning.
Lian X; Qi J; Yuan M; Li X; Wang M; Li G; Yang T; Zhong J
BMC Med Inform Decis Mak; 2023 Aug; 23(1):146. PubMed ID: 37533059
[TBL] [Abstract][Full Text] [Related]
13. A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression.
Usman TM; Saheed YK; Nsang A; Ajibesin A; Rakshit S
Artif Intell Med; 2023 Sep; 143():102617. PubMed ID: 37673580
[TBL] [Abstract][Full Text] [Related]
14. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
Wang J; Chen H; Wang H; Liu W; Peng D; Zhao Q; Xiao M
J Med Internet Res; 2023 Apr; 25():e43815. PubMed ID: 37023416
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India.
Vyas A; Raman S; Sen S; Ramasamy K; Rajalakshmi R; Mohan V; Raman R
Diagnostics (Basel); 2023 Jun; 13(12):. PubMed ID: 37370980
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model.
Wang Q; Zeng N; Tang H; Yang X; Yao Q; Zhang L; Zhang H; Zhang Y; Nie X; Liao X; Jiang F
Front Endocrinol (Lausanne); 2022; 13():993423. PubMed ID: 36465620
[TBL] [Abstract][Full Text] [Related]
20. Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning.
Wang X; Meng L; Zhang J; Zhao Z; Zou L; Jia Z; Han X; Zhao L; Song M; Zong J; Wang S; Qu X; Lu M
Front Endocrinol (Lausanne); 2023; 14():1189513. PubMed ID: 37645416
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
