Machine Learning Algorithms are Superior to Conventional Regression Models in Predicting Risk Stratification of COVID-19 Patients

References

• Fan Z, Chen L, Li J, et al. Clinical features of COVID-19-related liver functional abnormality. Clin Gastroenterol Hepatol. 2020;18(7):1561–1566. doi:10.1016/j.cgh.2020.04.002
   PubMed Web of Science ®Google Scholar
• Wu J, Song S, Cao HC, Li LJ. Liver diseases in COVID-19: etiology, treatment and prognosis. World J Gastroenterol. 2020;26(19):2286–2293. doi:10.3748/wjg.v26.i19.2286
   PubMed Web of Science ®Google Scholar
• Martins-Filho PR, Tavares CSS, Santos VS. Factors associated with mortality in patients with COVID-19. A quantitative evidence synthesis of clinical and laboratory data. Eur J Intern Med. 2020;76:97–99. doi:10.1016/j.ejim.2020.04.043
   PubMed Web of Science ®Google Scholar
• Liu Q, Song NC, Zheng ZK, Li JS, Li SK. Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: a Retrospective Study. Epidemiol Infect. 2020;148:e129. doi:10.1017/S0950268820001442
   Web of Science ®Google Scholar
• Wang F, Hou H, Luo Y, et al. The laboratory tests and host immunity of COVID-19 patients with different severity of illness. JCI Insight. 2020;5(10):e137799. doi:10.1172/jci.insight.137799
   Web of Science ®Google Scholar
• Vultaggio A, Vivarelli E, Virgili G, et al. Prompt predicting of early clinical deterioration of moderate-to-severe COVID-19 patients: usefulness of a combined score using IL-6 in a Preliminary Study. J Allergy Clin Immunol Pract. 2020;8(8):2575–2581.e2. doi:10.1016/j.jaip.2020.06.013
   Web of Science ®Google Scholar
• Wang F, Hou H, Wang T, et al. Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests. Travel Med Infect Dis. 2020;36:101782. doi:10.1016/j.tmaid.2020.101782
   Web of Science ®Google Scholar
• Zeng Z, Ma Y, Zeng H, et al. Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter Retrospective Study. J Med Virol. 2021;93(1):434–440. doi:10.1002/jmv.26244
   PubMed Web of Science ®Google Scholar
• Liu YP, Li GM, He J, et al. Combined use of the neutrophil-to-lymphocyte ratio and CRP to predict 7-day disease severity in 84 hospitalized patients with COVID-19 pneumonia: a Retrospective Cohort Study. Ann Transl Med. 2020;8(10):635. doi:10.21037/atm-20-2372
   PubMed Web of Science ®Google Scholar
• Albahri AS, Hamid RA, Alwan JK, et al. Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. J Med Syst. 2020;44(7):122. doi:10.1007/s10916-020-01582-x
   PubMed Web of Science ®Google Scholar
• Aggarwal L, Goswami P, Sachdeva S. Multi-criterion intelligent decision support system for COVID-19. Appl Soft Comput. 2021;101:107056. doi:10.1016/j.asoc.2020.107056
   Web of Science ®Google Scholar
• Guan X, Zhang B, Fu M, et al. Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a Retrospective Cohort Study. Ann Med. 2021;53(1):257–266. doi:10.1080/07853890.2020.1868564
   PubMed Web of Science ®Google Scholar
• National Health Commission. Diagnosis and treatment protocol for novel coronavirus pneumonia (trial version 7). Chin Med J (Engl). 2020;133(9):1087–1095. doi:10.1097/CM9.0000000000000819
   PubMed Web of Science ®Google Scholar
• Ye J, Zhang X, Zhu F, Tang YJE. Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID-19. Medicine. 2021;21(3):182.
   Web of Science ®Google Scholar
• Livne M, Boldsen JK, Mikkelsen IK, Fiebach JB, Sobesky J, Mouridsen K. Boosted tree model reforms multimodal magnetic resonance imaging infarct prediction in acute stroke. Stroke. 2018;49(4):912–918. doi:10.1161/STROKEAHA.117.019440
   PubMed Web of Science ®Google Scholar
• Wang K, Zuo P, Liu Y, et al. Clinical and laboratory predictors of in-hospital mortality in patients with coronavirus disease-2019: a Cohort Study in Wuhan, China. Clin Infect Dis. 2020;71(16):2079–2088. doi:10.1093/cid/ciaa538
   PubMed Web of Science ®Google Scholar
• Toniati P, Piva S, Cattalini M, et al. Tocilizumab for the treatment of severe COVID-19 pneumonia with hyperinflammatory syndrome and acute respiratory failure: a Single Center Study of 100 patients in Brescia, Italy. Autoimmun Rev. 2020;19(7):102568. doi:10.1016/j.autrev.2020.102568
   PubMed Web of Science ®Google Scholar
• Liu Y, Yang Y, Zhang C, et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci. 2020;63(3):364–374. doi:10.1007/s11427-020-1643-8
   PubMed Web of Science ®Google Scholar
• Barton LM, Duval EJ, Stroberg E, Ghosh S, Mukhopadhyay S. COVID-19 autopsies, Oklahoma, USA. Am J Clin Pathol. 2020;153(6):725–733. doi:10.1093/ajcp/aqaa062
   PubMed Web of Science ®Google Scholar
• Kapur R, Kim M, Shanmugabhavananthan S, Liu J, Li Y, Semple JW. C-reactive protein enhances murine antibody-mediated transfusion-related acute lung injury. Blood. 2015;126(25):2747–2751. doi:10.1182/blood-2015-09-672592
   PubMed Web of Science ®Google Scholar
• Saeed K, Dale AP, Leung E, et al. Procalcitonin levels predict infectious complications and response to treatment in patients undergoing cytoreductive surgery for peritoneal malignancy. Eur J Surg Oncol. 2016;42(2):234–243. doi:10.1016/j.ejso.2015.10.004
   Web of Science ®Google Scholar
• Choi JJ, McCarthy MW. Novel applications for serum procalcitonin testing in clinical practice. Expert Rev Mol Diagn. 2018;18(1):27–34. doi:10.1080/14737159.2018.1407244
   PubMed Web of Science ®Google Scholar
• Sager R, Kutz A, Mueller B, Schuetz P. Procalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med. 2017;15(1):15. doi:10.1186/s12916-017-0795-7
   PubMed Web of Science ®Google Scholar
• Hu R, Han C, Pei S, Yin M, Chen X. Procalcitonin levels in COVID-19 patients. Int J Antimicrob Agents. 2020;56(2):106051. doi:10.1016/j.ijantimicag.2020.106051
   PubMed Web of Science ®Google Scholar
• Liu F, Li L, Xu M, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127:104370. doi:10.1016/j.jcv.2020.104370
   PubMed Web of Science ®Google Scholar
• Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020;324(8):782. doi:10.1001/jama.2020.12839
   PubMed Web of Science ®Google Scholar
• Gheblawi M, Wang K, Viveiros A, et al. Angiotensin-converting enzyme 2: SARS-CoV-2 receptor and regulator of the renin-angiotensin system: celebrating the 20th anniversary of the discovery of ACE2. Circ Res. 2020;126(10):1456–1474. doi:10.1161/CIRCRESAHA.120.317015
   PubMed Web of Science ®Google Scholar
• Bavishi C, Maddox TM, Messerli FH. Coronavirus disease 2019 (COVID-19) infection and renin angiotensin system blockers. JAMA Cardiol. 2020;5(7):745–747. doi:10.1001/jamacardio.2020.1282
   PubMed Web of Science ®Google Scholar
• Wang L, He W, Yu X, et al. Coronavirus disease 2019 in elderly patients: characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020;80(6):639–645. doi:10.1016/j.jinf.2020.03.019
   PubMed Web of Science ®Google Scholar
• Ou M, Zhu J, Ji P, et al. Risk factors of severe cases with COVID-19: a meta-analysis. Epidemiol Infect. 2020;148:e175. doi:10.1017/S095026882000179X
   PubMed Web of Science ®Google Scholar
• Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi:10.1136/bmj.m1328
   PubMed Web of Science ®Google Scholar
• Zhang B, Zhang J, Chen H, et al. Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters. Eur J Nucl Med Mol Imaging. 2020;47(9):2083–2089. doi:10.1007/s00259-020-04854-3
   Web of Science ®Google Scholar
• Ghahramani S, Tabrizi R, Lankarani KB, et al. Laboratory features of severe vs. non-severe COVID-19 patients in Asian populations: a systematic review and meta-analysis. Eur J Med Res. 2020;25(1):30. doi:10.1186/s40001-020-00432-3
   PubMed Web of Science ®Google Scholar
• Assaf D, Gutman Y, Neuman Y, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435–1443. doi:10.1007/s11739-020-02475-0
   PubMed Web of Science ®Google Scholar
• Pan P, Li Y, Xiao Y, et al. Prognostic assessment of COVID-19 in the intensive care unit by machine learning methods: model development and validation. J Med Internet Res. 2020;22(11):e23128. doi:10.2196/23128
   Web of Science ®Google Scholar
• Fernandez DM, Cernadas E, Barro S, Amorim DJ. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133–3181.
   Web of Science ®Google Scholar