BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Chou EH, Wang CH, Hsieh YL, Namazi B, Wolfshohl J, Bhakta T, Tsai CL, Lien WC, Sankaranarayanan G, Lee CC, Lu TC. Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic that Predict SARS-CoV-2 Infection: Machine-learning Approach. West J Emerg Med 2021;22:244-51. [PMID: 33856307 DOI: 10.5811/westjem.2020.12.49370] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Şanlitürk D, Yilmaz A. Evaluation of Covid-19 Triage Assessment Scale in Patients Attending the Emergency Department. Journal of Basic and Clinical Health Sciences. [DOI: 10.30621/jbachs.959016] [Reference Citation Analysis]
2 Nahari AD, Son MBF, Newburger JW, Reis BY. An integrated framework for identifying clinical-laboratory indicators for novel pandemics: COVID-19 and MIS-C. npj Digit Med 2022;5. [DOI: 10.1038/s41746-021-00547-9] [Reference Citation Analysis]
3 Banoei MM, Dinparastisaleh R, Zadeh AV, Mirsaeidi M. Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying. Crit Care 2021;25:328. [PMID: 34496940 DOI: 10.1186/s13054-021-03749-5] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
4 Ortíz-Barrios MA, Coba-Blanco DM, Alfaro-Saíz JJ, Stand-González D. Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review. Int J Environ Res Public Health 2021;18:8814. [PMID: 34444561 DOI: 10.3390/ijerph18168814] [Reference Citation Analysis]