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For: Schalekamp S, Huisman M, van Dijk RA, Boomsma MF, Freire Jorge PJ, de Boer WS, Herder GJM, Bonarius M, Groot OA, Jong E, Schreuder A, Schaefer-Prokop CM. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19. Radiology 2021;298:E46-54. [PMID: 32787701 DOI: 10.1148/radiol.2020202723] [Cited by in Crossref: 17] [Cited by in F6Publishing: 17] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 Tariq A, Celi LA, Newsome JM, Purkayastha S, Bhatia NK, Trivedi H, Gichoya JW, Banerjee I. Patient-specific COVID-19 resource utilization prediction using fusion AI model. NPJ Digit Med 2021;4:94. [PMID: 34083734 DOI: 10.1038/s41746-021-00461-0] [Reference Citation Analysis]
2 Bartolucci M, Benelli M, Betti M, Bicchi S, Fedeli L, Giannelli F, Aquilini D, Baldini A, Consales G, Di Natale ME, Lotti P, Vannucchi L, Trezzi M, Mazzoni LN, Santini S, Carpi R, Matarrese D, Bernardi L, Mascalchi M; COVID Working Group. The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission. Sci Rep 2021;11:15619. [PMID: 34341411 DOI: 10.1038/s41598-021-95114-3] [Reference Citation Analysis]
3 Zhang B, Liu Q, Zhang X, Liu S, Chen W, You J, Chen Q, Li M, Chen Z, Chen L, Chen L, Dong Y, Zeng Q, Zhang S. Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis. Front Med (Lausanne) 2020;7:590460. [PMID: 33425939 DOI: 10.3389/fmed.2020.590460] [Reference Citation Analysis]
4 Afshar-Oromieh A, Prosch H, Schaefer-Prokop C, Bohn KP, Alberts I, Mingels C, Thurnher M, Cumming P, Shi K, Peters A, Geleff S, Lan X, Wang F, Huber A, Gräni C, Heverhagen JT, Rominger A, Fontanellaz M, Schöder H, Christe A, Mougiakakou S, Ebner L. A comprehensive review of imaging findings in COVID-19 - status in early 2021. Eur J Nucl Med Mol Imaging 2021;48:2500-24. [PMID: 33932183 DOI: 10.1007/s00259-021-05375-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
5 Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-rivero AI, Etmann C, Mccague C, Beer L, Weir-mccall JR, Teng Z, Gkrania-klotsas E, Rudd JHF, Sala E, Schönlieb C; AIX-COVNET. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 2021;3:199-217. [DOI: 10.1038/s42256-021-00307-0] [Cited by in Crossref: 65] [Cited by in F6Publishing: 13] [Article Influence: 65.0] [Reference Citation Analysis]
6 Scharf G, Meiler S, Zeman F, Schaible J, Poschenrieder F, Knobloch C, Kleine H, Scharf SE, Dinkel J, Stroszczynski C, Zorger N, Hamer OW. Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019. Rofo 2022. [PMID: 35272354 DOI: 10.1055/a-1731-7905] [Reference Citation Analysis]
7 Adarve Castro A, Díaz Antonio T, Cuartero Martínez E, García Gallardo MM, Bermá Gascón ML, Domínguez Pinos D. Usefulness of chest X-rays for evaluating prognosis in patients with COVID-19. Radiologia (Engl Ed) 2021;63:476-83. [PMID: 34801180 DOI: 10.1016/j.rxeng.2021.05.001] [Reference Citation Analysis]
8 Garrafa E, Vezzoli M, Ravanelli M, Farina D, Borghesi A, Calza S, Maroldi R. Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score. Elife 2021;10:e70640. [PMID: 34661530 DOI: 10.7554/eLife.70640] [Reference Citation Analysis]
9 Cotton DM, Liu L, Vinson DR, Ballard DW, Sax DR, Hofmann ER, Lin JS, Durant EJ, Kene MV, Casey SD, Ghiya M, Shan J, Bouvet SC, McLachlan ID, Rauchwerger AS, Mark DG, Reed ME; Clinical Research on Emergency Services and Treatment (CREST) Network. Clinical characteristics of COVID-19 patients evaluated in the emergency department: A retrospective cohort study of 801 cases. J Am Coll Emerg Physicians Open 2021;2:e12538. [PMID: 34467264 DOI: 10.1002/emp2.12538] [Reference Citation Analysis]
10 Plasencia-Martínez JM, Carrillo-Alcaraz A, Martín-Cascón M, Pérez-Costa R, Ballesta-Ruiz M, Blanco-Barrio A, Herves-Escobedo I, Gómez-Verdú JM, Alcaraz-Martínez J, Alemán-Belando S, Carrillo-Burgos MJ. Early radiological worsening of SARS-CoV-2 pneumonia predicts the need for ventilatory support. Eur Radiol 2022. [PMID: 35034140 DOI: 10.1007/s00330-021-08418-3] [Reference Citation Analysis]
11 Luo L, Fu M, Li Y, Hu S, Luo J, Chen Z, Yu J, Li W, Dong R, Yang Y, Tu L, Xu X. The potential association between common comorbidities and severity and mortality of coronavirus disease 2019: A pooled analysis. Clin Cardiol 2020;43:1478-93. [PMID: 33026120 DOI: 10.1002/clc.23465] [Cited by in Crossref: 25] [Cited by in F6Publishing: 25] [Article Influence: 12.5] [Reference Citation Analysis]
12 Calvillo-batllés P, Cerdá-alberich L, Fonfría-esparcia C, Carreres-ortega A, Muñoz-núñez C, Trilles-olaso L, Martí-bonmatí L. Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray. Radiología (English Edition) 2022. [DOI: 10.1016/j.rxeng.2021.09.004] [Reference Citation Analysis]
13 Wang H, Sun B, Li X, Wang Y, Yang Z. Clinical analysis of severe COVID-19 patients. THC 2022. [DOI: 10.3233/thc-228021] [Reference Citation Analysis]
14 Alharbi KS, Singh Y, Hassan Almalki W, Rawat S, Afzal O, Alfawaz Altamimi AS, Kazmi I, Al-Abbasi FA, Alzarea SI, Singh SK, Bhatt S, Chellappan DK, Dua K, Gupta G. Gut Microbiota Disruption in COVID-19 or Post-COVID Illness Association with severity biomarkers: A Possible Role of Pre / Pro-biotics in manipulating microflora. Chem Biol Interact 2022;:109898. [PMID: 35331679 DOI: 10.1016/j.cbi.2022.109898] [Reference Citation Analysis]
15 Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L; National Traditional Chinese Medicine Medical Team. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty 2022;11:19. [PMID: 35177120 DOI: 10.1186/s40249-022-00946-4] [Reference Citation Analysis]
16 Ramphul K, Lohana P, Ramphul Y, Park Y, Mejias S, Dhillon BK, Sombans S, Verma R. Hypertension, diabetes mellitus, and cerebrovascular disease predispose to a more severe outcome of COVID-19. Arch Med Sci Atheroscler Dis 2021;6:e30-9. [PMID: 34027212 DOI: 10.5114/amsad.2021.105255] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Yoon SH, Ham SY, Nam BD, Chae KJ, Lee D, Yoo JY, Bak SH, Kim JY, Kim JH, Kim KB, Jung JI, Lim JK, Lee JE, Chung MJ, Lee YK, Kim YS, Jo JE, Lee SM, Kwon W, Park CM, Kim YH, Jeong YJ. Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019. J Korean Med Sci 2020;35:e413. [PMID: 33258333 DOI: 10.3346/jkms.2020.35.e413] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Pyrros A, Flanders AE, Rodríguez-Fernández JM, Chen A, Cole P, Wenzke D, Hart E, Harford S, Horowitz J, Nikolaidis P, Muzaffar N, Boddipalli V, Nebhrajani J, Siddiqui N, Willis M, Darabi H, Koyejo O, Galanter W. Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning. Acad Radiol 2021;28:1151-8. [PMID: 34134940 DOI: 10.1016/j.acra.2021.05.002] [Reference Citation Analysis]
19 Petite Felipe DJ, Rivera Campos MI, San Miguel Espinosa J, Malo Rubio Y, Flores Quan JC, Cuartero Revilla MV. Initial findings in chest X-rays as predictors of worsening lung infection in patients with COVID-19: correlation in 265 patients. Radiologia 2021;63:324-33. [PMID: 33902938 DOI: 10.1016/j.rx.2021.03.004] [Reference Citation Analysis]
20 Casiraghi E, Malchiodi D, Trucco G, Frasca M, Cappelletti L, Fontana T, Esposito AA, Avola E, Jachetti A, Reese J, Rizzi A, Robinson PN, Valentini G. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE Access 2020;8:196299-325. [PMID: 34812365 DOI: 10.1109/ACCESS.2020.3034032] [Cited by in Crossref: 16] [Article Influence: 16.0] [Reference Citation Analysis]
21 Little BP. Disease Severity Scoring for COVID-19: A Welcome (Semi)Quantitative Role for Chest Radiography. Radiology 2021;:212212. [PMID: 34519581 DOI: 10.1148/radiol.2021212212] [Reference Citation Analysis]
22 Patel D, Kher V, Desai B, Lei X, Cen S, Nanda N, Gholamrezanezhad A, Duddalwar V, Varghese B, Oberai AA. Machine learning based predictors for COVID-19 disease severity. Sci Rep 2021;11:4673. [PMID: 33633145 DOI: 10.1038/s41598-021-83967-7] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
23 Petite Felipe DJ, Rivera Campos MI, San Miguel Espinosa J, Malo Rubio Y, Flores Quan JC, Cuartero Revilla MV. Initial findings in chest X-rays as predictors of worsening lung infection in patients with COVID-19: correlation in 265 patients. Radiologia (Engl Ed) 2021;63:324-33. [PMID: 34246423 DOI: 10.1016/j.rxeng.2021.03.006] [Reference Citation Analysis]
24 Oudkerk M, Büller HR, Kuijpers D, Oudkerk SF, van Beek EJR. d-Dimer and COVID-19. Radiology 2020;297:E343-4. [PMID: 33048037 DOI: 10.1148/radiol.2020203481] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Adarve Castro A, Díaz Antonio T, Cuartero Martínez E, García Gallardo MM, Bermá Gascón ML, Domínguez Pinos D. Usefulness of chest X-rays for evaluating prognosis in patients with COVID-19. Radiologia (Engl Ed) 2021:S0033-8338(21)00106-5. [PMID: 34243977 DOI: 10.1016/j.rx.2021.05.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Oh B, Hwangbo S, Jung T, Min K, Lee C, Apio C, Lee H, Lee S, Moon MK, Kim SW, Park T. Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study. J Med Internet Res 2021;23:e25852. [PMID: 33822738 DOI: 10.2196/25852] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Esposito A, Casiraghi E, Chiaraviglio F, Scarabelli A, Stellato E, Plensich G, Lastella G, Di Meglio L, Fusco S, Avola E, Jachetti A, Giannitto C, Malchiodi D, Frasca M, Beheshti A, Robinson PN, Valentini G, Forzenigo L, Carrafiello G. Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System. RMI 2021;Volume 14:27-39. [DOI: 10.2147/rmi.s292314] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
28 Daniyal M, Ogundokun RO, Abid K, Khan MD, Ogundokun OE. Predictive modeling of COVID-19 death cases in Pakistan. Infect Dis Model 2020;5:897-904. [PMID: 33195884 DOI: 10.1016/j.idm.2020.10.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
29 Bottino F, Tagliente E, Pasquini L, Napoli AD, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. J Pers Med 2021;11:893. [PMID: 34575670 DOI: 10.3390/jpm11090893] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]