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References
Kanne JP (2020) Chest CT Findings in 2019 Novel Coronavirus (2019-nCoV) Infections from Wuhan, China: Key Points for the Radiologist. Radiology 295(1):16–17
Rodriguez-Morales AJ, Cardona-Ospina JA, Gutierrez-Ocampo E, et al (2020) Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis 34:101623. https://doi.org/10.1016/j.tmaid.2020.101623
World Health Organization; WHO Director-General’s opening remarks at the media briefing on COVID-19 – 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19%2D%2D-11-march-2020
Insider; A CDC graph shows just how different the Omicron wave is compared to previous COVID-19 surges. https://www.businessinsider.com/cdc-graph-shows-difference-between-omicron-variant-previous-coronavirus-surges-2022-1?r=US&IR=T. Accessed October 18, 2022
Worldometer; COVID-19 Coronavirus Pandemic; Coronavirus cases. https://www.worldometers.info/coronavirus
Muniz-Rodriguez K, Fung IC-H, Ferdosi SR, et al (2020) Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020. Emerg Infect Dis 26(8):1915–1917
Musa S (2020) Hepatic and gastrointestinal involvement in coronavirus disease 2019 (COVID-19): What do we know till now? Arab J Gastroenterol 21(1):3–8
Boettler T, Newsome PN, Mondelli MU, et al (2020) Care of patients with liver disease during the COVID-19 pandemic: EASL-ESCMID position paper. HEP Rep 2(3):100113. https://doi.org/10.1016/j.jhepr.2020.100113
Matthay MA, Zemans RL, Zimmerman GA, et al (2019) Acute respiratory distress syndrome. Nat Rev Dis Primers 5(1):18. https://doi.org/10.1038/s41572-019-0069-0
Kim H (2020) Outbreak of novel coronavirus (COVID-19): What is the role of radiologists? Eur Radiol 30(6):3266–3267
Bhat R, Hamid A, Kunin JR, et al (2020) Chest Imaging in Patients Hospitalized With COVID-19 Infection – A Case Series. Curr Probl Diagn Radiol 49(4):294–301
Jafari R, Ashtari S, Pourhoseingholi MA, et al (2021) Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study. Adv Exp Med Biol 1321, 265–275.
Song YY, Lu Y (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27(2):130–135
Zimmerman RK, Balasubramani GK, Nowalk MP, et al (2016) Classification and Regression Tree (CART) analysis to predict influenza in primary care patients. BMC Infect Dis 16(1):503. https://doi.org/10.1186/s12879-016-1839-x
World Health Organization. Clinical management of severe acute respiratory infection when novel coronavirus (nCoV) infection is suspected: interim guidance. Published January 28, 2020. https://www.who.int/publications-detail/clinical-managementof-severe-acute-respiratory-infection-when-novelcoronavirus-(ncov)-infection-is-suspected. Accessed January 31, 2020
Schoen K, Horvat N, Guerreiro NFC, et al (2019) Spectrum of clinical and radiographic findings in patients with diagnosis of H1N1 and correlation with clinical severity. BMC Infect Dis 19(1):964. https://doi.org/10.1186/s12879-019-4592-0
Hansell DM, Bankier AA, MacMahon H, et al (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246(3):697–722
Chang YC, Yu CJ, Chang SC, et al (2005) Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: evaluation with thin-section CT. Radiology 236(3):1067–1075
Ricciardi C, Cantoni V, Improta G, et al (2020) Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center. Comput Methods Programs Biomed 189:105343. https://doi.org/10.1016/j.cmpb.2020.105343
Pérez-Guaita D, Quintás G, Kuligowski J (2020) Discriminant analysis and feature selection in mass spectrometry imaging using constrained repeated random sampling-Cross validation (CORRS-CV). Analytica Chimica Acta 1097:30–36
Mishra A, Basumallick S, Lu A, et al (2021) The healthier healthcare management models for COVID-19. J Infect Public Health 14(7):927–937
Salehi S, Abedi A, Balakrishnan S, et al (2020) Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. Am J Roentgenol 215(1):87–93
Taylor EH, Marson EJ, Elhadi M, et al (2021) Factors associated with mortality in patients with COVID-19 admitted to intensive care: a systematic review and meta-analysis. Anaesthesia 76(9):1224–1232
Shi C, Wang L, Ye J, et al (2021) Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis. BMC Infect Dis 21(1):663. https://doi.org/10.1186/s12879-021-06369-0
Kouhpayeh H (2022) Clinical features predicting COVID-19 mortality risk. Eur J Transl Myol 32(2):10268. https://doi.org/10.4081/ejtm.2022.10268
Liu W, Tao ZW, Wang L, et al (2020) Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chin Med J 133(9):1032–1038
Albtoush OM, Al-Shdefat RB, Al-Akaileh A (2020) Chest CT Scan Features from 302 patients with COVID-19 in Jordan. Eur J Radiol Open 7:100295. https://doi.org/10.1016/j.ejro.2020.100295
Carotti M, Salaffi F, Sarzi-Puttini P, et al (2020) Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists. Radiol Med 125(7):636–646
Yoon SH, Lee KH, Kim JY, et al (2020) Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol 21(4):494–500
Franquet T (2011) Imaging of pulmonary viral pneumonia. Radiology 260(1):18–39
Koo HJ, Lim S, Choe J, et al (2018) Radiographic and CT Features of Viral Pneumonia. Radiographics : a review publication of the Radiological Society of North America, Inc 38(3):719–739
Li K, Wu J, Wu F, et al (2020) The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia. Invest Radiol 55(6):327–531
Li L, Qin L, Xu Z, et al (2020) Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology 20090. https://doi.org/10.1148/radiol.2020200905
Pourhoseingholi A, Vahedi M, Chaibakhsh S, et al (2021) Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features. Adv Exp Med Biol 1327:139–147
Arru C, Ebrahimian S, Falaschi Z, et al (2021) Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia. Clin Imaging 80:58–66
Sahebkar A, Abbasifard M, Chaibakhsh S, et al (2022) A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes. Methods Mol Biol 2511:395–404
Lassau N, Ammari S, Chouzenoux E, et al (2021) Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun 12(1):634. https://doi.org/10.1038/s41467-020-20657-4
Weikert T, Rapaka S, Grbic S, et al (2021) Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings. Korean J Radiol 22(6):994–1004
Safont B, Tarraso J, Rodriguez-Borja E, et al (2022) Lung Function, Radiological Findings and Biomarkers of Fibrogenesis in a Cohort of COVID-19 Patients Six Months After Hospital Discharge. Arch Bronconeumol 58(2):142–149
Jafari M, Akbari M, Navidkia M, et al (2022) Comparison of clinical, radiological and laboratory findings in discharged and dead patients with COVID-19. Vacunas 23:S36–S43
Esposito A, Palmisano A, Scotti GM, et al (2020) Why is chest CT important for early diagnosis of COVID-19? Prevalence matters. medRxiv. https://doi.org/10.1101/2020.03.30.20047985
Gempeler A, Griswold DP, Rosseau G, et al (2022) An Umbrella Review With Meta-Analysis of Chest Computed Tomography for Diagnosis of COVID-19: Considerations for Trauma Patient Management. Front Med (Lausanne) 9:900721. https://doi.org/10.3389/fmed.2022.900721
Peter H, Mattig E, Guest PC, Bier FF (2022) Lab-on-a-Chip Immunoassay for Prediction of Severe COVID-19 Disease. Methods Mol Biol 2511:235–244
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Talebi, A. et al. (2023). Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis. In: Guest , P.C. (eds) Application of Omic Techniques to Identify New Biomarkers and Drug Targets for COVID-19. Advances in Experimental Medicine and Biology(), vol 1412. Springer, Cham. https://doi.org/10.1007/978-3-031-28012-2_13
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