These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
166 related articles for article (PubMed ID: 32606434)
1. An artificial neural network approach for predicting hypertension using NHANES data. López-Martínez F; Núñez-Valdez ER; Crespo RG; García-Díaz V Sci Rep; 2020 Jun; 10(1):10620. PubMed ID: 32606434 [TBL] [Abstract][Full Text] [Related]
2. Hypertension and CKD: Kidney Early Evaluation Program (KEEP) and National Health and Nutrition Examination Survey (NHANES), 1999-2004. Rao MV; Qiu Y; Wang C; Bakris G Am J Kidney Dis; 2008 Apr; 51(4 Suppl 2):S30-7. PubMed ID: 18359406 [TBL] [Abstract][Full Text] [Related]
3. Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018. Lin Z; Cheng YT; Cheung BMY Ann Med; 2023 Dec; 55(1):2209336. PubMed ID: 37162442 [TBL] [Abstract][Full Text] [Related]
5. Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm. Oh J; Yun K; Maoz U; Kim TS; Chae JH J Affect Disord; 2019 Oct; 257():623-631. PubMed ID: 31357159 [TBL] [Abstract][Full Text] [Related]
6. A multi-parameterized artificial neural network for lung cancer risk prediction. Hart GR; Roffman DA; Decker R; Deng J PLoS One; 2018; 13(10):e0205264. PubMed ID: 30356283 [TBL] [Abstract][Full Text] [Related]
7. Hypertension: development of a prediction model to adjust self-reported hypertension prevalence at the community level. Mentz G; Schulz AJ; Mukherjee B; Ragunathan TE; Perkins DW; Israel BA BMC Health Serv Res; 2012 Sep; 12():312. PubMed ID: 22967264 [TBL] [Abstract][Full Text] [Related]
8. The association between atherogenic index of plasma and all-cause mortality and cardiovascular disease-specific mortality in hypertension patients: a retrospective cohort study of NHANES. Duiyimuhan G; Maimaiti N BMC Cardiovasc Disord; 2023 Sep; 23(1):452. PubMed ID: 37697281 [TBL] [Abstract][Full Text] [Related]
9. Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images. Kim YD; Noh KJ; Byun SJ; Lee S; Kim T; Sunwoo L; Lee KJ; Kang SH; Park KH; Park SJ Sci Rep; 2020 Mar; 10(1):4623. PubMed ID: 32165702 [TBL] [Abstract][Full Text] [Related]
10. [Association Between the Aggregate Index of Systemic Inflammation and Albuminuria: A Cross-Sectional Study of National Health and Nutrition Examination Survey 2007-2018]. Sun L; Huo X; Jia S; Chen X Sichuan Da Xue Xue Bao Yi Xue Ban; 2024 May; 55(3):671-679. PubMed ID: 38948283 [TBL] [Abstract][Full Text] [Related]
11. Total and abdominal obesity among rural Chinese women and the association with hypertension. Zhang X; Yao S; Sun G; Yu S; Sun Z; Zheng L; Xu C; Li J; Sun Y Nutrition; 2012 Jan; 28(1):46-52. PubMed ID: 21621392 [TBL] [Abstract][Full Text] [Related]
12. Comparison of cardiovascular risk factors for high brachial pulse pressure in blacks versus whites (Charleston Heart Study, Evans County Study, NHANES I and II Studies). Gazes PC; Lackland DT; Mountford WK; Gilbert GE; Harley RA Am J Cardiol; 2008 Dec; 102(11):1514-7. PubMed ID: 19026306 [TBL] [Abstract][Full Text] [Related]
13. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557 [TBL] [Abstract][Full Text] [Related]
14. Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method. Zhao H; Zhang X; Xu Y; Gao L; Ma Z; Sun Y; Wang W Front Public Health; 2021; 9():619429. PubMed ID: 34631636 [TBL] [Abstract][Full Text] [Related]
15. Application of machine learning algorithms to identify people with low bone density. Xu R; Chen Y; Yao Z; Wu W; Cui J; Wang R; Diao Y; Jin C; Hong Z; Li X Front Public Health; 2024; 12():1347219. PubMed ID: 38726233 [TBL] [Abstract][Full Text] [Related]
16. A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia. Chai SS; Cheah WL; Goh KL; Chang YHR; Sim KY; Chin KO Comput Math Methods Med; 2021; 2021():2794888. PubMed ID: 34917164 [TBL] [Abstract][Full Text] [Related]
17. Artificial neural network model effectively estimates muscle and fat mass using simple demographic and anthropometric measures. Pathak P; Panday SB; Ahn J Clin Nutr; 2022 Jan; 41(1):144-152. PubMed ID: 34879301 [TBL] [Abstract][Full Text] [Related]
18. Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study. Fu J; Cai W; Zeng B; He L; Bao L; Lin Z; Lin F; Hu W; Lin L; Huang H; Zheng S; Chen L; Zhou W; Lin Y; Fu F Int J Nurs Stud; 2022 Nov; 135():104341. PubMed ID: 36084529 [TBL] [Abstract][Full Text] [Related]
19. A mathematical model for determining age-specific diabetes incidence and prevalence using body mass index. Appuhamy JA; Kebreab E; France J Ann Epidemiol; 2013 May; 23(5):248-54. PubMed ID: 23608303 [TBL] [Abstract][Full Text] [Related]
20. Interview to study the determinants of hypertension in older adults in Taiwan: a population based cross-sectional survey. Tsai AC; Liou JC; Chang MC Asia Pac J Clin Nutr; 2007; 16(2):338-45. PubMed ID: 17468092 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]