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Cited by in F6Publishing

For:	Xu F, Zhu J, Sun N, Wang L, Xie C, Tang Q, Mao X, Fu X, Brickell A, Hao Y, Sun C. Development and validation of prediction models for hypertension risks in rural Chinese populations. J Glob Health 2019;9:020601. [PMID: 31788232 DOI: 10.7189/jogh.09.020601] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]

For:

Xu F, Zhu J, Sun N, Wang L, Xie C, Tang Q, Mao X, Fu X, Brickell A, Hao Y, Sun C. Development and validation of prediction models for hypertension risks in rural Chinese populations. J Glob Health 2019;9:020601. [PMID: 31788232 DOI: 10.7189/jogh.09.020601] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]

Number	Citing Articles
1	Qin L, Zhang Y, Yang X, Wang H. Development of the prediction model for hypertension in patients with idiopathic inflammatory myopathies. J Clin Hypertens (Greenwich) 2021;23:1556-66. [PMID: 33973700 DOI: 10.1111/jch.14267] [Reference Citation Analysis]
2	Deng X, Hou H, Wang X, Li Q, Li X, Yang Z, Wu H. Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China. Elife 2021;10:e66419. [PMID: 34047697 DOI: 10.7554/eLife.66419] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3	Lin CC, Li CI, Liu CS, Lin CH, Wang MC, Yang SY, Li TC. A risk scoring system to predict the risk of new-onset hypertension among patients with type 2 diabetes. J Clin Hypertens (Greenwich) 2021;23:1570-80. [PMID: 34251744 DOI: 10.1111/jch.14322] [Reference Citation Analysis]
4	Silva GFS, Fagundes TP, Teixeira BC, Chiavegatto Filho ADP. Machine Learning for Hypertension Prediction: a Systematic Review. Curr Hypertens Rep 2022. [PMID: 35731335 DOI: 10.1007/s11906-022-01212-6] [Reference Citation Analysis]
5	Chowdhury MZI, Naeem I, Quan H, Leung AA, Sikdar KC, O'Beirne M, Turin TC. Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis. PLoS One 2022;17:e0266334. [PMID: 35390039 DOI: 10.1371/journal.pone.0266334] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6	Oishi E, Hata J, Honda T, Sakata S, Chen S, Hirakawa Y, Yoshida D, Shibata M, Ohara T, Furuta Y, Kitazono T, Ninomiya T. Development of a risk prediction model for incident hypertension in Japanese individuals: the Hisayama Study. Hypertens Res 2021;44:1221-9. [PMID: 34059807 DOI: 10.1038/s41440-021-00673-7] [Reference Citation Analysis]