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Cited by in F6Publishing
For: Zheng B, Cai Y, Zeng F, Lin M, Zheng J, Chen W, Qin G, Guo Y. An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19. Biomed Res Int 2021;2021:8840835. [PMID: 33708997 DOI: 10.1155/2021/8840835] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
Number Citing Articles
1 Wang Y, Xu J, Wang Y, Hou H, Shi L, Yang H. Prevalence of comorbid tuberculosis amongst COVID-19 patients: A rapid review and meta-analysis. Int J Clin Pract 2021;75:e14867. [PMID: 34670351 DOI: 10.1111/ijcp.14867] [Reference Citation Analysis]
2 Degarege A, Naveed Z, Kabayundo J, Brett-major D. Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis. Pathogens 2022;11:563. [DOI: 10.3390/pathogens11050563] [Reference Citation Analysis]
3 Aggarwal AN, Agarwal R, Dhooria S, Prasad KT, Sehgal IS, Muthu V. Active pulmonary tuberculosis and coronavirus disease 2019: A systematic review and meta-analysis. PLoS One 2021;16:e0259006. [PMID: 34673822 DOI: 10.1371/journal.pone.0259006] [Reference Citation Analysis]
4 Mateo EM, Gómez JV, Tarazona A, García-Esparza MÁ, Mateo F. Comparative Analysis of Machine Learning Methods to Predict Growth of F. sporotrichioides and Production of T-2 and HT-2 Toxins in Treatments with Ethylene-Vinyl Alcohol Films Containing Pure Components of Essential Oils. Toxins (Basel) 2021;13:545. [PMID: 34437416 DOI: 10.3390/toxins13080545] [Reference Citation Analysis]
5 Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. Sensors (Basel) 2021;21:8045. [PMID: 34884045 DOI: 10.3390/s21238045] [Reference Citation Analysis]