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Cited by in F6Publishing
For: Luo Y, Xue Y, Song H, Tang G, Liu W, Bai H, Yuan X, Tong S, Wang F, Cai Y, Sun Z. Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection. J Infect 2022:S0163-4453(21)00666-6. [PMID: 34995637 DOI: 10.1016/j.jinf.2021.12.046] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Huang Y, Ai L, Wang X, Sun Z, Wang F. Review and Updates on the Diagnosis of Tuberculosis. J Clin Med 2022;11:5826. [PMID: 36233689 DOI: 10.3390/jcm11195826] [Reference Citation Analysis]
2 Yu J, Lin HH, Tseng KH, Tien N, Hsueh PR, Cho DY. Direct prediction of ceftazidime-resistant Stenotrophomonas maltophilia from routine MALDI-TOF mass spectra using machine learning. J Infect 2022:S0163-4453(22)00536-9. [PMID: 36096314 DOI: 10.1016/j.jinf.2022.09.005] [Reference Citation Analysis]
3 Yu Q, Yan J, Tian S, Weng W, Luo H, Wei G, Long G, Ma J, Gong F, Wang X. A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis. Front Cell Infect Microbiol 2022;12:947954. [DOI: 10.3389/fcimb.2022.947954] [Reference Citation Analysis]
4 Liu Q, Chen X, Liu X, Yang D, Li T, Jiang L, Ji D, Dai X. Cervical lymph node dissection on the treatment of cervical tuberculosis. J Infect 2022:S0163-4453(22)00242-0. [PMID: 35483455 DOI: 10.1016/j.jinf.2022.04.036] [Reference Citation Analysis]