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For: Macedo Hair G, Fonseca Nobre F, Brasil P. Characterization of clinical patterns of dengue patients using an unsupervised machine learning approach. BMC Infect Dis 2019;19:649. [PMID: 31331271 DOI: 10.1186/s12879-019-4282-y] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
Number Citing Articles
1 Jusof FF, Lim CK, Aziz FN, Soe HJ, Raju CS, Sekaran SD, Guillemin GJ. Cytokines, CXCL10 and CCL2 and kynurenine metabolites, anthranilic acid accurately predicts patients at risk of developing Dengue with Warning Signs. J Infect Dis 2022:jiac273. [PMID: 35767283 DOI: 10.1093/infdis/jiac273] [Reference Citation Analysis]
2 Millar JE, Neyton L, Seth S, Dunning J, Merson L, Murthy S, Russell CD, Keating S, Swets M, Sudre CH, Spector TD, Ourselin S, Steves CJ, Wolf J, Docherty AB, Harrison EM, Openshaw PJM, Semple MG, Baillie JK; ISARIC-4C. Distinct clinical symptom patterns in patients hospitalised with COVID-19 in an analysis of 59,011 patients in the ISARIC-4C study. Sci Rep 2022;12:6843. [PMID: 35478198 DOI: 10.1038/s41598-022-08032-3] [Reference Citation Analysis]
3 He J, Li X. Identification and Validation of Aging-Related Genes in Idiopathic Pulmonary Fibrosis. Front Genet 2022;13:780010. [PMID: 35211155 DOI: 10.3389/fgene.2022.780010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Sylvestre E, Joachim C, Cécilia-Joseph E, Bouzillé G, Campillo-Gimenez B, Cuggia M, Cabié A. Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. PLoS Negl Trop Dis 2022;16:e0010056. [PMID: 34995281 DOI: 10.1371/journal.pntd.0010056] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Lee S, Kang S, Eun Y, Won HH, Kim H, Cha HS, Koh EM, Lee J. A cluster analysis of patients with axial spondyloarthritis using tumour necrosis factor alpha inhibitors based on clinical characteristics. Arthritis Res Ther 2021;23:284. [PMID: 34782006 DOI: 10.1186/s13075-021-02647-z] [Reference Citation Analysis]
6 Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif Intell Med 2021;119:102157. [PMID: 34531010 DOI: 10.1016/j.artmed.2021.102157] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
7 Webster AJ, Gaitskell K, Turnbull I, Cairns BJ, Clarke R. Characterisation, identification, clustering, and classification of disease. Sci Rep 2021;11:5405. [PMID: 33686097 DOI: 10.1038/s41598-021-84860-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
8 Salim NAM, Wah YB, Reeves C, Smith M, Yaacob WFW, Mudin RN, Dapari R, Sapri NNFF, Haque U. Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques. Sci Rep 2021;11:939. [PMID: 33441678 DOI: 10.1038/s41598-020-79193-2] [Cited by in Crossref: 7] [Cited by in F6Publishing: 11] [Article Influence: 7.0] [Reference Citation Analysis]
9 Sandri V, Gonçalves IL, Machado das Neves G, Romani Paraboni ML. Diagnostic significance of C-reactive protein and hematological parameters in acute toxoplasmosis. J Parasit Dis 2020;:1-9. [PMID: 32904402 DOI: 10.1007/s12639-020-01262-0] [Reference Citation Analysis]
10 [DOI: 10.1101/2020.08.14.20168088] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Reference Citation Analysis]