{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:45:43Z","timestamp":1767015943904,"version":"build-2065373602"},"reference-count":93,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T00:00:00Z","timestamp":1698710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beatriu de Pin\u00f3s post-doctoral programme","award":["2020 BP 00261","UIDB\/00297\/2020","UIDP\/00297\/2020","PID2020-117751RB-I00"],"award-info":[{"award-number":["2020 BP 00261","UIDB\/00297\/2020","UIDP\/00297\/2020","PID2020-117751RB-I00"]}]},{"name":"National Funds","award":["2020 BP 00261","UIDB\/00297\/2020","UIDP\/00297\/2020","PID2020-117751RB-I00"],"award-info":[{"award-number":["2020 BP 00261","UIDB\/00297\/2020","UIDP\/00297\/2020","PID2020-117751RB-I00"]}]},{"name":"Ministry of Science and Innovation","award":["2020 BP 00261","UIDB\/00297\/2020","UIDP\/00297\/2020","PID2020-117751RB-I00"],"award-info":[{"award-number":["2020 BP 00261","UIDB\/00297\/2020","UIDP\/00297\/2020","PID2020-117751RB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Optimal allocation of ward beds is crucial given the respiratory nature of COVID-19, which necessitates urgent hospitalization for certain patients. Several governments have leveraged technology to mitigate the pandemic\u2019s adverse impacts. Based on clinical and demographic variables assessed upon admission, this study predicts the length of stay (LOS) for COVID-19 patients in hospitals. The Kolmogorov\u2013Gabor polynomial (a.k.a., Volterra functional series) was trained using regularized least squares and validated on a dataset of 1600 COVID-19 patients admitted to Khorshid Hospital in the central province of Iran, and the five-fold internal cross-validated results were presented. The Volterra method provides flexibility, interactions among variables, and robustness. The most important features of the LOS prediction system were inflammatory markers, bicarbonate (HCO3), and fever\u2014the adj. R2 and Concordance Correlation Coefficients were 0.81 [95% CI: 0.79\u20130.84] and 0.94 [0.93\u20130.95], respectively. The estimation bias was not statistically significant (p-value = 0.777; paired-sample t-test). The system was further analyzed to predict \u201cnormal\u201d LOS \u2264 7 days versus \u201cprolonged\u201d LOS &gt; 7 days groups. It showed excellent balanced diagnostic accuracy and agreement rate. However, temporal and spatial validation must be considered to generalize the model. This contribution is hoped to pave the way for hospitals and healthcare providers to manage their resources better.<\/jats:p>","DOI":"10.3390\/info14110590","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T12:53:32Z","timestamp":1698756812000},"page":"590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting COVID-19 Hospital Stays with Kolmogorov\u2013Gabor Polynomials: Charting the Future of Care"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4408-2397","authenticated-orcid":false,"given":"Hamidreza","family":"Marateb","sequence":"first","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0311-6888","authenticated-orcid":false,"given":"Mina","family":"Norouzirad","sequence":"additional","affiliation":[{"name":"Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA SST), 2825-149 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5785-358X","authenticated-orcid":false,"given":"Kouhyar","family":"Tavakolian","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA"}]},{"given":"Faezeh","family":"Aminorroaya","sequence":"additional","affiliation":[{"name":"Epidemiology and Biostatistics Department, School of Health, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran"}]},{"given":"Mohammadreza","family":"Mohebbian","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9836-6083","authenticated-orcid":false,"given":"Miguel \u00c1ngel","family":"Ma\u00f1anas","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"},{"name":"CIBER de Bioingenier\u00eda, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8627-543X","authenticated-orcid":false,"given":"Sergio Romero","family":"Lafuente","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"},{"name":"CIBER de Bioingenier\u00eda, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain"}]},{"given":"Ramin","family":"Sami","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, School of Medicine, Isfahan University of Medical Science, Isfahan 81746-73461, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7217-0282","authenticated-orcid":false,"given":"Marjan","family":"Mansourian","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Polit\u00e8cnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, Spain"},{"name":"Epidemiology and Biostatistics Department, School of Health, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.21037\/atm-20-5248","article-title":"The shortage of hospital beds for COVID-19 and non-COVID-19 patients during the lockdown of Wuhan, China","volume":"9","author":"Zhuang","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jss.2020.11.062","article-title":"A Closer Look into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic","volume":"260","author":"Sutherland","year":"2021","journal-title":"J. Surg. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100570","DOI":"10.1016\/j.jemep.2020.100570","article-title":"Choosing which COVID-19 patient to save? The ethical triage and rationing dilemma","volume":"15","author":"Jaziri","year":"2020","journal-title":"Ethics Med. Public Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/S0140-6736(20)30627-9","article-title":"COVID-19 and Italy: What next?","volume":"395","author":"Remuzzi","year":"2020","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Deschepper, M., Eeckloo, K., Malfait, S., Benoit, D., Callens, S., and Vansteelandt, S. (2021). Prediction of hospital bed capacity during the COVID-19 pandemic. BMC Health Serv. Res., 21.","DOI":"10.1186\/s12913-021-06492-3"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pasquale, S., Gregorio, G.L., Caterina, A., Francesco, C., Beatrice, P.M., Vincenzo, P., and Caterina, P.M. (2021). COVID-19 in Low- and Middle-Income Countries (LMICs): A Narrative Review from Prevention to Vaccination Strategy. Vaccines, 9.","DOI":"10.3390\/vaccines9121477"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1213\/ANE.0000000000004866","article-title":"Provider Burnout and Fatigue During the COVID-19 Pandemic: Lessons Learned from a High-Volume Intensive Care Unit","volume":"131","author":"Sasangohar","year":"2020","journal-title":"Anesth. Analg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3934\/publichealth.2022008","article-title":"Nursing staff fatigue and burnout during the COVID-19 pandemic in Greece","volume":"9","author":"Sikaras","year":"2022","journal-title":"AIMS Public Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5382","DOI":"10.1111\/jocn.15566","article-title":"Insomnia, fatigue and psychosocial well-being during COVID-19 pandemic: A cross-sectional survey of hospital nursing staff in the United States","volume":"32","author":"Sagherian","year":"2023","journal-title":"J. Clin. Nurs."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alsunaidi, S.J., Almuhaideb, A.M., Ibrahim, N.M., Shaikh, F.S., Alqudaihi, K.S., Alhaidari, F.A., Khan, I.U., Aslam, N., and Alshahrani, M.S. (2021). Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors, 21.","DOI":"10.3390\/s21072282"},{"key":"ref_11","unstructured":"Marateb, H.R., Mohebbian, M.R., Shirzadi, M., Mirshamsi, A., Zamani, S., Abrisham chi, A., Bafande, F., and Ma\u00f1anas, M.\u00c1. (2021). High Performance Computing for Intelligent Medical Systems, IOP Publishing."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Steele, A.J., Denaxas, S.C., Shah, A.D., Hemingway, H., and Luscombe, N.M. (2018). Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PLoS ONE, 13.","DOI":"10.1101\/256008"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1152\/physiolgenomics.00029.2020","article-title":"Artificial intelligence and machine learning to fight COVID-19","volume":"52","author":"Alimadadi","year":"2020","journal-title":"Physiol. Genom."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.ijmedinf.2015.01.017","article-title":"The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: A pilot step-wedge cluster randomized trial","volume":"84","author":"Pickering","year":"2015","journal-title":"Int. J. Med. Inf. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100074","DOI":"10.1016\/j.patter.2020.100074","article-title":"Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data","volume":"1","author":"Nemati","year":"2020","journal-title":"Patterns"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"443","DOI":"10.21037\/atm.2020.03.147","article-title":"Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay","volume":"8","author":"Hong","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100035","DOI":"10.1016\/j.ibmed.2021.100035","article-title":"A Machine Learning Algorithm Predicts Duration of hospitalization in COVID-19 patients","volume":"5","author":"Ebinger","year":"2021","journal-title":"Intell. Based Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"ooab055","DOI":"10.1093\/jamiaopen\/ooab055","article-title":"Overcoming gaps: Regional collaborative to optimize capacity management and predict length of stay of patients admitted with COVID-19","volume":"4","author":"Usher","year":"2021","journal-title":"JAMIA Open"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"592336","DOI":"10.3389\/fmed.2021.592336","article-title":"Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling","volume":"8","author":"Mahboub","year":"2021","journal-title":"Front. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11517-021-02479-8","article-title":"Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution","volume":"60","author":"Liuzzi","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Orooji, A., Shanbehzadeh, M., Mirbagheri, E., and Kazemi-Arpanahi, H. (2022). Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19. BMC Infect. Dis., 22.","DOI":"10.1186\/s12879-022-07921-2"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100937","DOI":"10.1016\/j.imu.2022.100937","article-title":"Machine learning model for predicting the length of stay in the intensive care unit for COVID-19 patients in the eastern province of Saudi Arabia","volume":"30","author":"Alabbad","year":"2022","journal-title":"Inf. Inform. Med. Unlocked"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alam, F., Ananbeh, O., Malik, K.M., Odayani, A.A., Hussain, I.B., Kaabia, N., Aidaroos, A.A., and Saudagar, A.K.J. (2023). Towards Predicting Length of Stay and Identification of Cohort Risk Factors Using Self-Attention-Based Transformers and Association Mining: COVID-19 as a Phenotype. Diagnostics, 13.","DOI":"10.20944\/preprints202301.0341.v1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2196177","DOI":"10.1080\/21505594.2023.2196177","article-title":"Development of a model by LASSO to predict hospital length of stay (LOS) in patients with the SARS-CoV-2 omicron variant","volume":"14","author":"Zhang","year":"2023","journal-title":"Virulence"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.jclinepi.2012.04.008","article-title":"Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias","volume":"65","author":"Wolkewitz","year":"2012","journal-title":"J. Clin. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pepe, M.S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press.","DOI":"10.1093\/oso\/9780198509844.001.0001"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Steyerberg, E.W. (2019). Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, Springer.","DOI":"10.1007\/978-3-030-16399-0"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sami, R., Soltaninejad, F., Amra, B., Naderi, Z., Haghjooy Javanmard, S., Iraj, B., Haji Ahmadi, S., Shayganfar, A., Dehghan, M., and Khademi, N. (2020). A one-year hospital-based prospective COVID-19 open-cohort in the Eastern Mediterranean region: The Khorshid COVID Cohort (KCC) study. PLoS ONE, 15.","DOI":"10.1101\/2020.05.11.20096727"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/0021-9681(87)90171-8","article-title":"A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation","volume":"40","author":"Charlson","year":"1987","journal-title":"J. Chronic Dis."},{"key":"ref_30","first-page":"188","article-title":"Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation","volume":"12","author":"Glasheen","year":"2019","journal-title":"Am. Health Drug Benefits"},{"key":"ref_31","first-page":"117","article-title":"Does the Charlson comorbidity index help predict the risk of death in COVID-19 patients?","volume":"9","author":"Comoglu","year":"2022","journal-title":"North. Clin. Istanb."},{"key":"ref_32","unstructured":"Walker, H., Hall, W., and Hurst, J. (1990). Clinical Methods: The History, Physical, and Laboratory Examinations, Butterworths. [3rd ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical Characteristics of Coronavirus Disease 2019 in China","volume":"382","author":"Guan","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_34","first-page":"17-11","article-title":"Rigorous performance assessment of computer-aided medical diagnosis and prognosis systems: A biostatistical perspective on data mining","volume":"2","author":"Mansourian","year":"2020","journal-title":"Model. Anal. Act. Biopotential Signals Healthc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"141","DOI":"10.11613\/BM.2015.015","article-title":"Understanding Bland Altman analysis","volume":"25","author":"Giavarina","year":"2015","journal-title":"Biochem. Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ofori-Asenso, R., Liew, D., M\u00e5rtensson, J., and Jones, D. (2020). The Frequency of, and Factors Associated with Prolonged Hospitalization: A Multicentre Study in Victoria, Australia. J. Clin. Med., 9.","DOI":"10.3390\/jcm9093055"},{"key":"ref_37","unstructured":"Madala, H.R., and Ivakhnenko, A.G.e. (1994). Inductive Learning Algorithms for Complex Systems Modeling, CRC Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40537-020-00305-w","article-title":"Survey on categorical data for neural networks","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"ref_39","first-page":"9","article-title":"A Study of Effects of MultiCollinearity in the Multivariable Analysis","volume":"4","author":"Yoo","year":"2014","journal-title":"Int. J. Appl. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Beck, A. (2014). Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB, Society for Industrial and Applied Mathematics, Mathematical Optimization Society.","DOI":"10.1137\/1.9781611973655"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/0010-4809(85)90014-X","article-title":"Ridge regression and its application to medical data","volume":"18","author":"Jain","year":"1985","journal-title":"Comput. Biomed. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A Concordance Correlation Coefficient to Evaluate Reproducibility","volume":"45","author":"Lawrence","year":"1989","journal-title":"Biometrics"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rees, E.M., Nightingale, E.S., Jafari, Y., Waterlow, N.R., Clifford, S., Pearson, C.A.B., Group, C.W., Jombart, T., Procter, S.R., and Knight, G.M. (2020). COVID-19 length of hospital stay: A systematic review and data synthesis. BMC Med., 18.","DOI":"10.1186\/s12916-020-01726-3"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chicco, D., T\u00f6tsch, N., and Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Min., 14.","DOI":"10.1186\/s13040-021-00244-z"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Collins, G.S., Reitsma, J.B., Altman, D.G., and Moons, K.G.M. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement. BMC Med., 13.","DOI":"10.1186\/s12916-014-0241-z"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yanez, N.D., Weiss, N.S., Romand, J.-A., and Treggiari, M.M. (2020). COVID-19 mortality risk for older men and women. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-09826-8"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13354","DOI":"10.1038\/s41598-023-40414-z","article-title":"Body temperature as a predictor of mortality in COVID-19","volume":"13","author":"Uchiyama","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"285","DOI":"10.2478\/jtim-2021-0042","article-title":"Elevated resting heart rates are a risk factor for mortality among patients with coronavirus disease 2019 in Wuhan, China","volume":"9","author":"Jin","year":"2021","journal-title":"J. Transl. Int. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e2255815","DOI":"10.1001\/jamanetworkopen.2022.55815","article-title":"Identification of Bradycardia Following Remdesivir Administration Through the US Food and Drug Administration American College of Medical Toxicology COVID-19 Toxic Pharmacovigilance Project","volume":"6","author":"Devgun","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1136\/bmj.326.7399.1104-a","article-title":"US guidelines say blood pressure of 120\/80 mm Hg is not \u201cnormal\u201d","volume":"326","year":"2003","journal-title":"BMJ"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mej\u00eda, F., Medina, C., Cornejo, E., Morello, E., V\u00e1squez, S., Alave, J., Schwalb, A., and M\u00e1laga, G. (2020). Oxygen saturation as a predictor of mortality in hospitalized adult patients with COVID-19 in a public hospital in Lima, Peru. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0244171"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s00277-020-04103-5","article-title":"Hematological findings in coronavirus disease 2019: Indications of progression of disease","volume":"99","author":"Liu","year":"2020","journal-title":"Ann. Hematol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e23618","DOI":"10.1002\/jcla.23618","article-title":"Ferritin in the coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis","volume":"34","author":"Cheng","year":"2020","journal-title":"J. Clin. Lab. Anal."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1093\/ije\/dyab012","article-title":"The role of C-reactive protein as a prognostic marker in COVID-19","volume":"50","author":"Stringer","year":"2021","journal-title":"Int. J. Epidemiol."},{"key":"ref_57","first-page":"76","article-title":"Raised erythrocyte sedimentation rate signals heart failure in patients with rheumatoid arthritis","volume":"66","author":"Nicola","year":"2007","journal-title":"Ann. Rheum. Dis."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Nakakubo, S., Unoki, Y., Kitajima, K., Terada, M., Gatanaga, H., Ohmagari, N., Yokota, I., and Konno, S. (2023). Serum Lactate Dehydrogenase Level One Week after Admission Is the Strongest Predictor of Prognosis of COVID-19: A Large Observational Study Using the COVID-19 Registry Japan. Viruses, 15.","DOI":"10.3390\/v15030671"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"120","DOI":"10.14218\/JCTH.2020.00100","article-title":"Elevated Liver Enzymes along with Comorbidity Is a High Risk Factor for COVID-19 Mortality: A South Indian Study on 1512 Patients","volume":"10","author":"Krishnasamy","year":"2022","journal-title":"J. Clin. Transl. Hepatol."},{"key":"ref_60","first-page":"98","article-title":"Elevated Alt and Ast in an Asymptomatic Person: What the primary care doctor should do?","volume":"4","author":"Yin","year":"2009","journal-title":"Malays. Fam. Physician"},{"key":"ref_61","unstructured":"Walker, H.K., Hall, W.D., and Hurst, J.W. (1990). Clinical Methods: The History, Physical, and Laboratory Examinations, Butterworth Publishers."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/S0002-9343(00)00500-3","article-title":"Relation between length of hospital stay and costs of care for patients with community-acquired pneumonia","volume":"109","author":"Fine","year":"2000","journal-title":"Am. J. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jemermed.2012.05.007","article-title":"Boarding inpatients in the emergency department increases discharged patient length of stay","volume":"44","author":"White","year":"2013","journal-title":"J. Emerg. Med."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chang, R., Elhusseiny, K.M., Yeh, Y.-C., and Sun, W.-Z. (2021). COVID-19 ICU and mechanical ventilation patient characteristics and outcomes\u2014A systematic review and meta-analysis. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0246318"},{"key":"ref_65","unstructured":"Group, I.C.C., Baillie, J.K., Joaquin, B., Abigail, B., Lucille, B., Fernando Augusto, B., Tessa, B., Aidan, B., Gail, C., and Barbara Wanjiru, C. (2022). ISARIC COVID-19 Clinical Data Report issued: 27 March 2022. medRxiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"839","DOI":"10.2147\/JMDH.S304788","article-title":"Predictors of Length of Hospital Stay, Mortality, and Outcomes Among Hospitalised COVID-19 Patients in Saudi Arabia: A Cross-Sectional Study","volume":"14","author":"Alwafi","year":"2021","journal-title":"J. Multidiscip. Healthc."},{"key":"ref_67","unstructured":"Garbacz, S. (2023, August 26). Average COVID-19 Hospital Stay Greater than Three Weeks. Available online: https:\/\/www.kpcnews.com\/covid-19\/article_8ab408ad-8fb0-5f74-8d57-11e586bd8a4f.html."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T., Chinn, J., De Ferrante, M., Kirby, K.A., Hohmann, S.F., and Amin, A. (2021). Male gender is a predictor of higher mortality in hospitalized adults with COVID-19. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254066"},{"key":"ref_69","unstructured":"Commission, E. (2023, August 26). Hospital Discharges and Length of Stay Statistics. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Hospital_discharges_and_length_of_stay_statistics&oldid=561104#Average_length_of_hospital_stay_for_in-patients."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"33","DOI":"10.7326\/M20-3905","article-title":"Patient Trajectories Among Persons Hospitalized for COVID-19: A Cohort Study","volume":"174","author":"Garibaldi","year":"2021","journal-title":"Ann. Intern. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/S2213-2600(20)30316-7","article-title":"Case characteristics, resource use, and outcomes of 10\u2008021 patients with COVID-19 admitted to 920 German hospitals: An observational study","volume":"8","author":"Karagiannidis","year":"2020","journal-title":"Lancet Respir. Med."},{"key":"ref_72","first-page":"E304","article-title":"Determine the most common clinical symptoms in COVID-19 patients: A systematic review and meta-analysis","volume":"61","author":"Alimohamadi","year":"2020","journal-title":"J. Prev. Med. Hyg."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/s41392-020-0148-4","article-title":"Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study","volume":"5","author":"Tan","year":"2020","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_75","unstructured":"Henry, B., Cheruiyot, I., Vikse, J., Mutua, V., Kipkorir, V., Benoit, J., Plebani, M., Bragazzi, N., and Lippi, G. (2020). Lymphopenia and neutrophilia at admission predicts severity and mortality in patients with COVID-19: A meta-analysis. Acta Biomed., 91."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jaci.2020.05.003","article-title":"Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China","volume":"146","author":"Chen","year":"2020","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e23609","DOI":"10.1002\/jcla.23609","article-title":"Correlations of disease severity and age with hematology parameter variations in patients with COVID-19 pre- and post-treatment","volume":"35","author":"Liang","year":"2021","journal-title":"J. Clin. Lab. Anal."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"664108","DOI":"10.3389\/fpubh.2021.664108","article-title":"Prognostic Role of Neutrophil to Lymphocyte Ratio in COVID-19 Patients: Still Valid in Patients That Had Started Therapy?","volume":"9","author":"Gelzo","year":"2021","journal-title":"Front. Public Health"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s11739-021-02924-4","article-title":"Clusters of inflammation in COVID-19: Descriptive analysis and prognosis on more than 15,000 patients from the Spanish SEMI-COVID-19 Registry","volume":"17","author":"Formiga","year":"2022","journal-title":"Intern. Emerg. Med."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s11739-020-02443-8","article-title":"COVID-19: The crucial role of blood coagulation and fibrinolysis","volume":"15","author":"Coccheri","year":"2020","journal-title":"Intern. Emerg. Med."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1111\/ejh.13501","article-title":"COVID-19 coagulopathy: An in-depth analysis of the coagulation system","volume":"105","author":"Monsalvo","year":"2020","journal-title":"Eur. J. Haematol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"101623","DOI":"10.1016\/j.tmaid.2020.101623","article-title":"Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis","volume":"34","year":"2020","journal-title":"Travel Med. Infect. Dis."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"440","DOI":"10.3855\/jidc.12678","article-title":"Epidemiological and clinical characteristics of COVID-19 patients in Nantong, China","volume":"14","author":"Lu","year":"2020","journal-title":"J. Infect. Dev. Ctries."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1515\/cclm-2020-0369","article-title":"Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): A meta-analysis","volume":"58","author":"Henry","year":"2020","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1080\/00365513.2020.1768587","article-title":"Laboratory findings of COVID-19: A systematic review and meta-analysis","volume":"80","author":"Zhang","year":"2020","journal-title":"Scand. J. Clin. Lab. Investig."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.2147\/IJGM.S321581","article-title":"Association Between the Admission Serum Bicarbonate and Short-Term and Long-Term Mortality in Acute Aortic Dissection Patients Admitted to the Intensive Care Unit","volume":"14","author":"Tan","year":"2021","journal-title":"Int. J. Gen. Med."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1016\/j.jacc.2018.02.028","article-title":"Hypotensive Systolic Blood Pressure Predicts Severe Complications and In-Hospital Mortality in Acute Aortic Dissection","volume":"71","author":"Erbel","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.mayocp.2019.05.036","article-title":"Serum Bicarbonate Concentration and Cause-Specific Mortality: The National Health and Nutrition Examination Survey 1999-2010","volume":"95","author":"Sarode","year":"2020","journal-title":"Mayo Clin. Proc."},{"key":"ref_89","unstructured":"(2014). GMDH-Methodology and Implementation in MATLAB, Imperial College Press."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"b2393","DOI":"10.1136\/bmj.b2393","article-title":"Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls","volume":"338","author":"Sterne","year":"2009","journal-title":"BMJ"},{"key":"ref_91","first-page":"548","article-title":"Improvements on Cross-Validation: The 632+ Bootstrap Method","volume":"92","author":"Efron","year":"1997","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"102216","DOI":"10.1016\/j.media.2021.102216","article-title":"AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study","volume":"74","author":"Soda","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_93","first-page":"200148","article-title":"A multi-modal bone suppression, lung segmentation, and classification approach for accurate COVID-19 detection using chest radiographs","volume":"16","author":"Rani","year":"2022","journal-title":"Intell. Syst. Appl."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/11\/590\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:14:36Z","timestamp":1760130876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/11\/590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,31]]},"references-count":93,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["info14110590"],"URL":"https:\/\/doi.org\/10.3390\/info14110590","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2023,10,31]]}}}