Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymak...

Full description

Autores:: Ortiz Barrios, Miguel Angel
Arias Fonseca, Sebastian
Ishizaka, Alessio
Barbati, Maria
Avendano-Collante, Betty
Navarro Jiménez, Eduardo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2023

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

id	RCUC2_ff8fcef536f99a9f75ad5fd0b57f684a
oai_identifier_str	oai:repositorio.cuc.edu.co:11323/10447
network_acronym_str	RCUC2
network_name_str	REDICUC - Repositorio CUC
repository_id_str
dc.title.none.fl_str_mv	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
title	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
spellingShingle	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study Covid-19 Discrete-Event Simulation (DES) Artificial Intelligence (AI) Random Forest (RF) Intensive Care Unit (ICU) Healthcare
title_short	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
title_full	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
title_fullStr	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
title_full_unstemmed	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
title_sort	Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study
dc.creator.fl_str_mv	Ortiz Barrios, Miguel Angel Arias Fonseca, Sebastian Ishizaka, Alessio Barbati, Maria Avendano-Collante, Betty Navarro Jiménez, Eduardo
dc.contributor.author.none.fl_str_mv	Ortiz Barrios, Miguel Angel Arias Fonseca, Sebastian Ishizaka, Alessio Barbati, Maria Avendano-Collante, Betty Navarro Jiménez, Eduardo
dc.subject.proposal.eng.fl_str_mv	Covid-19 Discrete-Event Simulation (DES) Artificial Intelligence (AI) Random Forest (RF) Intensive Care Unit (ICU) Healthcare
topic	Covid-19 Discrete-Event Simulation (DES) Artificial Intelligence (AI) Random Forest (RF) Intensive Care Unit (ICU) Healthcare
description	The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-09-06T14:09:32Z
dc.date.available.none.fl_str_mv	2023-09-06T14:09:32Z 2026
dc.date.issued.none.fl_str_mv	2023
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
format	http://purl.org/coar/resource_type/c_2df8fbb1
status_str	publishedVersion
dc.identifier.citation.spa.fl_str_mv	Miguel Ortiz-Barrios, Sebastián Arias-Fonseca, Alessio Ishizaka, Maria Barbati, Betty Avendaño-Collante, Eduardo Navarro-Jiménez, Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study, Journal of Business Research, Volume 160, 2023, 113806, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2023.113806
dc.identifier.issn.spa.fl_str_mv	0148-2963
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/11323/10447
dc.identifier.doi.none.fl_str_mv	10.1016/j.jbusres.2023.113806
dc.identifier.eissn.spa.fl_str_mv	1873-7978
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
identifier_str_mv	Miguel Ortiz-Barrios, Sebastián Arias-Fonseca, Alessio Ishizaka, Maria Barbati, Betty Avendaño-Collante, Eduardo Navarro-Jiménez, Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study, Journal of Business Research, Volume 160, 2023, 113806, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2023.113806 0148-2963 10.1016/j.jbusres.2023.113806 1873-7978 Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/10447 https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.ispartofjournal.spa.fl_str_mv	Journal of Business Research
dc.relation.references.spa.fl_str_mv	Abdalkareem, Z. A., Amir, A., Al-Betar, M. A., Ekhan, P., & Hammouri, A. I. (2021). Healthcare scheduling in optimization context: A review. Health and Technology, 11 (3), 445–469. Agrawal, R., Wankhede, V. A., Kumar, A., Upadhyay, A., & Garza-Reyes, J. A. (2021). Nexus of circular economy and sustainable business performance in the era of digitalization. International Journal of Productivity and Performance Management, 71 (3), 748–774. Ahmad, J., Iqbal, J., Ahmad, I., Khan, Z. A., Tiwana, M. I., & Khan, K. (2020). A simulation based study for managing hospital resources by reducing patient waiting time. IEEE Access, 8, 193523–193531. https://doi.org/10.1109/ ACCESS.2020.3032760 Akhavan, A. R., Habboushe, J. P., Gulati, R., Iheagwara, O., Watterson, J., Thomas, S., … Lee, D. C. (2020). Risk stratification of covid-19 patients using ambulatory oxygen saturation in the emergency department. Western Journal of Emergency Medicine, 21 (6). https://doi.org/10.5811/WESTJEM.2020.8.48701 Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons and Fractals, 140. https://doi.org/10.1016/j. chaos.2020.110120 Alballa, N., & Al-Turaiki, I. (2021). Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked, 24, Article 100564. https://doi.org/10.1016/j.imu.2021.100564 Albitar, O., Ballouze, R., Ooi, J. P., & Sheikh Ghadzi, S. M. (2020). Risk factors for mortality among COVID-19 patients. Diabetes Research and Clinical Practice, 166, Article 108293. https://doi.org/10.1016/j.diabres.2020.108293 Almeida, A., & Vales, J. (2020). The impact of primary health care reform on hospital emergency department overcrowding: Evidence from the portuguese reform. International Journal of Health Planning and Management, 35(1), 368–377. https://doi. org/10.1002/hpm.2939 Aly, M. H., Rahman, S. S., Ahmed, W. A., Alghamedi, M. H., Al Shehri, A. A., Alkalkami, A. M., & Hassan, M. H. (2020). Indicators of critical illness and predictors of mortality in COVID-19 patients. Infection and Drug Resistance, 13, 1995–2000. https://doi.org/10.2147/IDR.S261159 Amantea, I. A., Di Leva, A., & Sulis, E. (2020). A simulation-driven approach to decision support in process reorganization: A case study in healthcare. Lecture Notes in Information Systems and Organisation, 33, 223–235. https://doi.org/10.1007/978-3- 030-23665-6_16 Assaf, D., Gutman, Y., Neuman, Y., Segal, G., Amit, S., Gefen-Halevi, S., … Tirosh, A. (2020). Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Internal and Emergency Medicine, 15(8), 1435–1443. https://doi. org/10.1007/s11739-020-02475-0 Aznar-Gimeno, R., Esteban, L. M., Labata-Lezaun, G., Del-Hoyo-alonso, R., AbadiaGallego, D., Pano-Pardo, ˜ J. R., Esquillor-Rodrigo, M. J., Lanas, A., ´ & Serrano, M. T. (2021). A clinical decision web to predict ICU admission or death for patients hospitalised with COVID-19 using machine learning algorithms. International Journal of Environmental Research and Public Health, 18(16). https://doi.org/10.3390/ ijerph18168677 Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., … Mackenzie, L. S. (2020). Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. International Immunopharmacology, 86, Article 106705. https://doi.org/10.1016/j. intimp.2020.106705 Barrios, M. A. O., Caballero, J. E., & Sanchez, ´ F. S. (2015). A methodology for the creation of integrated service networks in outpatient internal medicine. doi: 10.1007/978-3-319- 26508-7_24. Batista, G. E. A. P. A., & Monard, M. C. (2003). An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5–6), 519–533. https://doi.org/10.1080/713827181 Behl, A., Gaur, J., Pereira, V., Yadav, R., & Laker, B. (2022). Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19–A multi-theoretical approach. Journal of Business Research, 148, 378–389. Belgiu, M., & Dragut ˘ ¸, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. Bihri, H., Hsaini, S., Nejjari, R., Azzouzi, S., & Charaf M.E.H. (2022). Missing data analysis in the healthcare field: COVID-19 case study. In Networking, intelligent systems and security. Smart innovation, systems and technologies (Vol. 237). Ssingapore: Springer. doi: 10.1007/978-981-16-3637-0_61. Birkmeyer, J. D., Barnato, A., Birkmeyer, N., Bessler, R., & Skinner, J. (2020). The impact of the COVID-19 pandemic on hospital admissions in the United States: Study examines trends in US hospital admissions during the COVID-19 pandemic. Health Affairs, 39(11), 2010–2017. https://doi.org/10.1377/hlthaff.2020.00980 Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial intelligence in healthcare (pp. 25–60). Academic Press. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/ 10.1023/A:1010933404324 Brennan, A., Chick, S. E., & Davies, R. (2006). A taxonomy of model structures for economic evaluation of health technologies. Health economics, 15(12), 1295–1310. Caillon, A., Zhao, K., Klein, K. O., Greenwood, C. M. T., Lu, Z., Paradis, P., & Schiffrin, E. L. (2021). High systolic blood pressure at hospital admission is an important risk factor in models predicting outcome of COVID-19 patients. American Journal of Hypertension, 34(3), 282–290. https://doi.org/10.1093/ajh/hpaa225 Caro, J. J., Moller, ¨ J., & Getsios, D. (2010). Discrete event simulation: The preferred technique for Health Economic Evaluations? Value in Health, 13(8), 1056–1060. https://doi.org/10.1111/j.1524-4733.2010.00775.x Caro, J. J., Moller, ¨ J., Santhirapala, V., Gill, H., Johnston, J., El-Boghdadly, K., Santhirapala, R., Kelly, P., & McGuire, A. (2021). Predicting hospital resource use during COVID-19 surges: A simple but flexible discretely integrated condition event simulation of individual patient-hospital trajectories. Value in Health, 24(11), 1570–1577. https://doi.org/10.1016/j.jval.2021.05.023 Casier, G., Casier, K., Van Ooteghem, J., & Verbrugge, S. (2015). Application of a discrete event simulator for healthcare processes. In BMSD 2015 - Proceedings of the 5th international symposium on business modeling and software design. https://doi.org/ 10.5220/0005887702410246 Casiraghi, E., Malchiodi, D., Trucco, G., Frasca, M., Cappelletti, L., Fontana, T., … Valentini, G. (2020). Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments. IEEE Access, 8, 196299–196325. https:// doi.org/10.1109/ACCESS.2020.3034032 Castanheira-Pinto, A., Gonçalves, B. S., Lima, R. M., & Dinis-Carvalho, J. (2021). Modeling, assessment and design of an emergency department of a public hospital through discrete-event simulation. Applied Sciences (Switzerland), 11(2), 1–25. https://doi.org/10.3390/app11020805 Chakravorty, T., Jha, K., & Barthwal, S. (2018). Digital technologies as enablers of carequality and performance: A conceptual review of hospital supply chain network. IUP Journal of Supply Chain Management, 15(3), 7–25. Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2021). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research, 1–26. Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential guide for health leaders. In Healthcare management forum (Vol. 33, No. 1, pp. 10–18). Los Angeles, CA: SAGE Publications. Cheng, F. Y., Joshi, H., Tandon, P., Freeman, R., Reich, D. L., Mazumdar, M., Kohli-seth, R., Levin, M., Timsina, P., & Kia, A. (2020). Using machine learning to predict ICU transfer in hospitalized COVID-19 patients. Journal of Clinical Medicine, 9(6). doi: 10.3390/jcm9061668. Cheng, A., Hu, L., Wang, Y., Huang, L., Zhao, L., Zhang, C., … Liu, Q. (2020). Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. International Journal of Antimicrobial Agents, 56(3). https://doi.org/10.1016/j.ijantimicag.2020.106110 Choi, Y., Lee, H., & Irani, Z. (2018). Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Annals of Operations Research, 270(1–2), 75–104. https://doi.org/10.1007/s10479-016-2281-6 Choron, R. L., Butts, C. A., Bargoud, C., Krumrei, N. J., Teichman, A. L., Schroeder, M. E., Bover Manderski, M. T., Cai, J., Song, C., Rodricks, M. B., Lissauer, M., & Gupta, R. (2021). Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID19 Patients. Journal of Intensive Care Medicine, 36(4), 484–493. https://doi.org/ 10.1177/0885066620979622 Chowdhury, M., Cervantes, E. G., Chan, W. Y., & Seitz, D. P. (2021). Use of machine learning and artificial intelligence methods in geriatric mental health research involving electronic health record or administrative claims data: A systematic review. Frontiers Psychiatry, 12(September). https://doi.org/10.3389/ fpsyt.2021.738466 Corro Ramos, I., Hoogendoorn, M., & Rutten-van Molken, ¨ M. P. M. H. (2020). How to address uncertainty in health economic discrete-event simulation models: An illustration for chronic obstructive pulmonary disease. Medical Decision Making, 40 (5), 619–632. https://doi.org/10.1177/0272989X20932145 Currie, C. S. M., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID19. Journal of Simulation, 14(2), 83–97. https://doi.org/10.1080/ 17477778.2020.1751570 Davis, Z., Zobel, C. W., Khansa, L., & Glick, R. E. (2020). Emergency department resilience to disaster-level overcrowding: A component resilience framework for analysis and predictive modeling. Journal of Operations Management, 66(1–2), 54–66. https://doi.org/10.1002/joom.1017 De Santis, A., Giovannelli, T., Lucidi, S., Messedaglia, M., & Roma, M. (2021). Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of emergency department patient arrivals. Flexible Services and Manufacturing Journal, 1–34. https://doi.org/10.1007/s10696-021-09408-9 Dergaa, I., Abubaker, M., Souissi, A., Mohammed, A. R., Varma, A., Musa, S., Al Naama, A., Mkaouer, B., & Saad, H. B. (2022). Age and clinical signs as predictors of COVID19 symptoms and cycle threshold value. Libyan Journal of Medicine, 17(1). doi: 10.1080/19932820.2021.2010337. Di Castelnuovo, A., Bonaccio, M., Costanzo, S., Gialluisi, A., Antinori, A., Berselli, N., … COVID, T. (2020). Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: Survival analysis and machine learning-based findings from the multicentre Italian CORIST study. Nutrition, Metabolism and Cardiovascular Diseases, 30(11), 1899–1913. doi:10.1016/j.numecd.2020.07.031. Doneda, M., Yalçındag, ˘ S., Marques, I., & Lanzarone, E. (2021). A discrete-event simulation model for analysing and improving operations in a blood donation centre. Vox Sanguinis, 116(10), 1060–1075. https://doi.org/10.1111/vox.13111 Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284–289. https://doi.org/10.1016/j. jbusres.2020.06.008 Drewry, A. M., Hotchkiss, R., & Kulstad, E. (2020). Response to “body temperature correlates with mortality in COVID-19 patients”. Critical Care, 24(1). https://doi. org/10.1186/s13054-020-03186-w Ellahham, S., Ellahham, N., & Simsekler, M. C. E. (2020). Application of artificial intelligence in the health care safety context: Opportunities and challenges. American Journal of Medical Quality, 35(4), 341–348. https://doi.org/10.1177/ 1062860619878515 Famiglini, L., Campagner, A., Carobene, A., & Cabitza, F. (2022). A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients. Medical and Biological Engineering and Computing. , Article 0123456789. https://doi.org/10.1007/s11517-022-02543-x Fawagreh, K., & Gaber, M. M. (2020). Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach. Computing, 102(5), 1187–1198. Feng, C., Kephart, G., & Juarez-Colunga, E. (2021). Predicting COVID-19 mortality risk in Toronto, Canada: A comparison of tree-based and regression-based machine learning methods. BMC Medical Research Methodology, 21(1), 1–14. https://doi.org/10.1186/ s12874-021-01441-4 Fernandes, F. T., de Oliveira, T. A., Teixeira, C. E., Batista, A. F. de M., Dalla Costa, G., & Chiavegatto Filho, A. D. P. (2021). A multipurpose machine learning approach to predict COVID-19 negative prognosis in S˜ ao Paulo, Brazil. Scientific Reports, 11(1), 1–7. doi: 10.1038/s41598-021-82885-y. Figliozzi, S., Masci, P. G., Ahmadi, N., Tondi, L., Koutli, E., Aimo, A., … Georgiopoulos, G. (2020). Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis. European Journal of Clinical Investigation, 50(10). https:// doi.org/10.1111/eci.13362 Forbes (2022). AI for health and hope: How machine learning is being used in hospitals. Available at: https://www.forbes.com/sites/forbestechcouncil/2022/02/16/ai -for-health-and-hope-how-machine-learning-is-being-used-in-hospitals/ (Accessed: 10-10-2022). Frid-Adar, M., Amer, R., Gozes, O., Nassar, J., & Greenspan, H. (2021). COVID-19 in CXR: From detection and severity scoring to patient disease monitoring. IEEE Journal of Biomedical and Health Informatics, 25(6), 1892–1903. https://doi.org/10.1109/ JBHI.2021.3069169 Gallo Marin, B., Aghagoli, G., Lavine, K., Yang, L., Siff, E. J., Chiang, S. S., … Michelow, I. C. (2021). Predictors of COVID-19 severity: A literature review. Reviews in Medical Virology, 31(1), 1–10. https://doi.org/10.1002/rmv.2146 Garcia-Vicuna, ˜ D., Esparza, L., & Mallor, F. (2021). Hospital preparedness during epidemics using simulation: The case of COVID-19. Central European Journal of Operations Research. https://doi.org/10.1007/s10100-021-00779-w Garcia-Vicuna, D., Mallor, F., & Esparza, L. (2020). Planning ward and intensive care unit beds for COVID-19 patients using a discrete event simulation model. In Paper presented at the proceedings - winter simulation conference, 2020-December (pp. 759–770). doi: 10.1109/WSC48552.2020.9383939. Gillespie, J., McClean, S., Garg, L., Barton, M., Scotney, B., & Fullerton, K. (2016). A multi-phase DES modelling framework for patient-centred care. Journal of the Operational Research Society, 67(10), 1239–1249. https://doi.org/10.1057/ jors.2015.114 Gok, ¨ E. C., & Olgun, M. O. (2021). SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples. Neural Computing and Applications, 33(22), 15693–15707. https://doi.org/ 10.1007/s00521-021-06189-y Gopinath, N. (2021). Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic. Process Biochemistry, 110(August), 94–99. https://doi.org/10.1016/j. procbio.2021.08.001 Gorunescu, F., McClean, S. I., & Millard, P. H. (2002). A queueing model for bedoccupancy management and planning of hospitals. Journal of the Operational Research Society, 53(1), 19–24. Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, Article 101967. Griffin, D. O., Griffin, D. O., Jensen, A., Khan, M., Chin, J., Chin, K., … Patel, D. (2020). Pulmonary embolism and increased levels of D-dimer in patients with coronavirus disease. Emerging Infectious Diseases, 26(8), 1941–1943. https://doi.org/10.3201/ eid2608.201477 Gul, M., & Guneri, A. F. (2012). A computer simulation model to reduce patient length of stay and to improve resource utilization rate in an emergency department service system. International Journal of Industrial Engineering, 19(5), 221–231. 10.23055/ij ietap.2012.19.5.793. Gumaei, A., Ismail, W. N., Rafiul Hassan, M., Hassan, M. M., Mohamed, E., Alelaiwi, A., & Fortino, G. (2022). A decision-level fusion method for COVID-19 patient health prediction. Big Data Research, 27, Article 100287. https://doi.org/10.1016/j. bdr.2021.100287 Günal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation, 4, 42–51. https://doi. org/10.1057/jos.2009.25 Guo, T., Fan, Y., Chen, M., Xiaoyan, W.u., Zhang, L., He, T., Wang, H., Wan, J., Wang, X., & Zhibing, L.u. (2020). Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA Cardiology, 5(7), 811–888. https://doi.org/10.1001/jamacardio.2020.1017 Gupta, V. K., Gupta, A., Kumar, D., & Sardana, A. (2021). Prediction of COVID-19 confirmed, death, and cured cases in india using random forest model. Big Data Mining and Analytics, 4(2), 116–123. https://doi.org/10.26599/ BDMA.2020.9020016 Heineke, J. (1995). Strategic operations management decisions and professional performance in US HMOs. Journal of Operations Management, 13(4), 255–272. Heldt, F. S., Vizcaychipi, M. P., Peacock, S., Cinelli, M., McLachlan, L., Andreotti, F., … Khan, R. T. (2021). Early risk assessment for COVID-19 patients from emergency department data using machine learning. Scientific Reports, 11(1), 1–13. doi: 10.1038/s41598-021-83784-y. Hosmer, D. W. Jr., Lemeshow, S., Sturdivant, R.X. (2013). Applied logistic regression (Vol. 398). New York, NY: John Wiley & Sons. Hou, W., Zhao, Z., Chen, A., Li, H., & Duong, T. Q. (2021). Machining learning predicts the need for escalated care and mortality in covid-19 patients from clinical variables. International Journal of Medical Sciences, 18(8), 1739–1745. doi: 10.7150/ ijms.51235. Hsu, J., Hung, P., Lin, H., & Hsieh, C. (2015). Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer. Journal of Medical Systems, 39(4) doi: 10.1007/s10916-015-0210-x. Hu, J., Zhou, J., Dong, F., Tan, J., Wang, S., Li, Z., … Huang, T. (2020). Combination of serum lactate dehydrogenase and sex is predictive of severe disease in patients with COVID-19. Medicine, 99(42), e22774. Huang, H. F., Liu, Y., Li, J. X., Dong, H., Gao, S., Huang, Z. Y., Fu, S. Z., Yang, L. Y., Lu, H. Z., Xia, L. Y., Cao, S., Gao, Y., & Yu, X. X. (2021). Validated tool for early prediction of intensive care unit admission in COVID-19 patients. World Journal of Clinical Cases, 9(28), 8388–8403. 10.12998/wjcc.v9.i28.8388. Huang, S., Yang, J., Fong, S., & Zhao, Q. (2021). Artificial intelligence in the diagnosis of COVID-19: Challenges and perspectives. International Journal of Biological Sciences, 17(6), 1581. https://doi.org/10.7150/ijbs.58855 Ikemura, K., Bellin, E., Yagi, Y., Billett, H., Saada, M., Simone, K., … Gil, M. R. (2021). Using automated machine learning to predict the mortality of patients with COVID19: Prediction model development study. Journal of Medical Internet Research, 23(2). https://doi.org/10.2196/23458 Irvine, N., Anderson, G., Sinha, C., McCabe, H., & Van der Meer, R. (2021). Collaborative critical care prediction and resource planning during the COVID-19 pandemic using computer simulation modelling: Future urgent planning lessons. Future Healthcare Journal, 8(2), e317–e321. https://doi.org/10.7861/fhj.2020-0194 Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of Big Data, 7(1), 1–22. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/ 00207543.2018.1488086 Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S., & Jo, O. (2020). COVID-19 patient health prediction using boosted random forest algorithm. Frontiers in Public Health, 8(July), 1–9. https://doi. org/10.3389/fpubh.2020.00357 Iyengar, K. P., Vaishya, R., Bahl, S., & Vaish, A. (2020). Impact of the coronavirus pandemic on the supply chain in healthcare. British Journal of Healthcare Management, 26(6), 1–4. Jack, E. P., & Powers, T. L. (2009). A review and synthesis of demand management, capacity management and performance in health-care services. International Journal of Management Reviews, 11, 149–174. https://doi.org/10.1111/j.1468- 2370.2008.00235.x Jadhav, S., Kasar, R., Lade, N., Patil, M., & Kolte, S. (2017). Disease prediction by machine learning from healthcare communities. International Journal of Scientific Research in Science and Technology, 29–35. 10.32628/ijsrst19633. Jarrett, M., Schultz, S., Lyall, J., Wang, J., Stier, L., de Geronimo, M., & Nelson, K. (2020). Clinical mortality in a large COVID-19 cohort: Observational study. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/23565 Jraisat, L., Jreissat, M., Upadhyay, A., & Kumar, A. (2022). Blockchain technology: The role of integrated reverse supply chain networks in sustainability. In Supply chain forum: An international journal (pp. 1–14). Taylor & Francis. Jun, J. B., Jacobson, S. H., & Swisher, J. R. (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society, 50(2), 109–123. https://doi.org/10.1057/palgrave.jors.2600669 Kabir, G., Tesfamariam, S., Hemsing, J., & Sadiq, R. (2020). Handling incomplete and missing data in water network database using imputation methods. Sustainable and Resilient Infrastructure, 5(6), 365–377. https://doi.org/10.1080/ 23789689.2019.1600960 Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38 (9), 1–20. Khalilia, M., Chakraborty, S., & Popescu, M. (2011). Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making, 11(1), 1–13. Kim, J., Lim, H., Ahn, J. H., Lee, K. H., Lee, K. S., & Koo, K. C. (2021). Optimal triage for COVID-19 patients under limited health care resources with a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation: Development study. JMIR Medical Informatics, 9(11), e32726. Klassen, K. J., & Rohleder, T. R. (2001). Combining operations and marketing to manage capacity and demand in services. Service Industries Journal, 21(2), 1–30. Kochan, C., Nowicki, D. R., Sauser, B., & Randall, W. S. (2018). Impact of cloud-based information sharing on hospital supply chain performance: A system dynamics framework. International Journal of Production Economics, 195, 168–185. https://doi. org/10.1016/j.ijpe.2017.10.008 Kortbeek, N., Braaksma, A., Smeenk, F. H., Bakker, P. J., & Boucherie, R. J. (2015). Integral resource capacity planning for inpatient care services based on bed census predictions by hour. Journal of the Operational Research Society, 66(7), 1061–1076. Kumar, A., & Mo, J. (2010). Models for bed occupancy management of a hospital in Singapore. In Proceedings of the 2010 international conference on industrial engineering and operations management (pp. 1–6). IIEOM & cosponsored by INFORMS. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, Article 110059. https://doi.org/10.1016/j. chaos.2020.110059 Le Lay, J., Augusto, V., Xie, X., Alfonso-Lizarazo, E., Bongue, B., Celarier, T., … Masmoudi, M. (2020). Impact of covid-19 epidemics on bed requirements in a healthcare center using data-driven discrete-event simulation. In 2020 Winter simulation conference (WSC) (pp. 771–781). IEEE. doi: 10.1109/ WSC48552.2020.9384093. Leung, C. (2020). Risk factors for predicting mortality in elderly patients with COVID-19: A review of clinical data in china. Mechanisms of Ageing and Development, 188. https://doi.org/10.1016/j.mad.2020.111255 Li, X., Ge, P., Zhu, J., Li, H., Graham, J., Singer, A., Richman, P. S., & Duong, T. Q. (2020). Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables. PeerJ, 8(December 2019), 1–19. https:// doi.org/10.7717/peerj.10337 Lin, G., Feng, X., Guo, W., Cui, X., Liu, S., Jin, W., … Ding, Y. (2021). Electricity theft detection based on stacked autoencoder and the undersampling and resampling based random forest algorithm. IEEE Access, 9, 124044–124058. https://doi.org/ 10.1109/ACCESS.2021.3110510 Long, B., Brady, W. J., Bridwell, R. E., Ramzy, M., Montrief, T., Singh, M., & Gottlieb, M. (2021). Electrocardiographic manifestations of COVID-19. American Journal of Emergency Medicine, 41, 96–103. https://doi.org/10.1016/j.ajem.2020.12.060 Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F., & Ozceylan, ¨ E. (2022). Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach. International Journal of Fuzzy Systems, 24(2), 1216–1231. Lu, Y., Guan, Y., Zhong, X., Fishe, J. N., & Hogan, T. (2021). Hospital beds planning and admission control policies for COVID-19 pandemic: A hybrid computer simulation approach. In IEEE international conference on automation science and engineering, 2021- Augus (pp. 956–961). doi: 10.1109/CASE49439.2021.9551589. Luo, L., Luo, L., Zhang, X., & He, X. (2017). Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research, 17(1), 1–13. Marwaha, J. S., Beaulieu-Jones, B. R., Kennedy, C. J., Nathanson, L. A., Robinson, K., Tandon, M., & Brat, G. A. (2022). Design, implementation, and clinical impact of a machine learning-assisted intervention bundle to improve opioid prescribing. NEJM Catalyst Innovations in Care Delivery, 3(4), CAT-21. Mauer, E., Lee, J., Choi, J., Zhang, H., Hoffman, K. L., Easthausen, I. J., … Banerjee, S. (2021). A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories. Journal of Biomedical Informatics, 118, Article 103794. https://doi.org/10.1016/j.jbi.2021.103794 McGlothlin, J. P., Vedire, S., Srinivasan, H., Madugula, A., Rajagopalan, S., & Khan, L. (2018). Predicting hospital capacity and efficiency. In HEALTHINF 2018 - 11th international conference on health informatics, proceedings; part of 11th international joint conference on biomedical engineering systems and technologies, BIOSTEC 2018, 5 (Biostec) (pp. 562–570). doi: 10.5220/0006658905620570. Mehmood, R., Meriton, R., Graham, G., Hennelly, P., & Kumar, M. (2017). Exploring the influence of big data on city transport operations: A markovian approach. International Journal of Operations and Production Management, 37(1), 75–104. https://doi.org/10.1108/IJOPM-03-2015-0179 Mehrabadi, M. A., Aqajari, S. A. H., Azimi, I., Downs, C. A., Dutt, N., & Rahmani, A. M. (2021). Detection of COVID-19 using heart rate and blood pressure: Lessons learned from patients with ARDS. In Paper presented at the proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS (pp. 2140–2143). doi: 10.1109/EMBC46164.2021.9629794. Meijboom, B., Schmidt-Bakx, S., & Westert, G. (2011). Supply chain management practices for improving patient-oriented care. Supply Chain Management, 16(3), 166–175. https://doi.org/10.1108/13598541111127155 Mejía, F., Medina, C., Cornejo, E., Morello, E., Vasquez, ´ S., Alave, J., … Malaga, ´ 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(12 December) doi: 10.1371/journal.pone.0244171. Melman, G. J., Parlikad, A. K., & Cameron, E. A. B. (2021). Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation. Health Care Management Science, 19, 356–374. https://doi.org/10.1007/s10729-021- 09548-2 Mercatelli, D., Formaggio, F., Caprini, M., Holding, A., & Giorgi, F. M. (2021). Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach. Bioscience Reports, 41(12). https://doi.org/10.1042/ BSR20212218 Mesas, A. E., Cavero-Redondo, I., Alvarez-Bueno, ´ C., Cabrera, M. A. S., de Andrade, S. M., Sequí-Dominguez, I., & Martínez-Vizcaíno, V. (2020). Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS ONE, 15(11 November). doi: 10.1371/journal.pone.0241742. Moledina, S. M., Maini, A. A., Gargan, A., Harland, W., Jenney, H., Phillips, G., Thomas, K., Chauhan, D., & Fertleman, M. (2020). Clinical characteristics and predictors of mortality in patients with COVID-19 infection outside intensive care. International Journal of General Medicine, 13, 1157–1165. https://doi.org/10.2147/ IJGM.S271432 More, A. S., & Rana, D. P. (2017). Review of random forest classification techniques to resolve data imbalance. In 2017 1st International conference on intelligent systems and information management (ICISIM) (pp. 72–78). Mudatsir, M., Fajar, J. K., Wulandari, L., Soegiarto, G., Ilmawan, M., Purnamasari, Y., … Harapan, H. (2021). Predictors of COVID-19 severity: A systematic review and metaanalysis [version 2; peer review: 2 approved]. F1000Research, 9, 1–26. doi: 10.12688/F1000RESEARCH.26186.2. Muhammad, N., Upadhyay, A., Kumar, A., & Gilani, H. (2022). Achieving operational excellence through the lens of lean and Six Sigma during the COVID-19 pandemic. The International Journal of Logistics Management. Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment. Naymagon, L., Zubizarreta, N., Feld, J., van Gerwen, M., Alsen, M., Thibaud, S., Kessler, A., Venugopal, S., Makki, I., Qin, Q., Dharmapuri, S., Jun, T., Bhalla, S., Berwick, S., Christian, K., Mascarenhas, J., Dembitzer, F., Moshier, E., & Tremblay, D. (2020). Admission D-dimer levels, D-dimer trends, and outcomes in COVID-19. Thrombosis Research, 196(June), 99–105. doi: 10.1016/j.thromres.2020.08.032. Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment. Naymagon, L., Zubizarreta, N., Feld, J., van Gerwen, M., Alsen, M., Thibaud, S., Kessler, A., Venugopal, S., Makki, I., Qin, Q., Dharmapuri, S., Jun, T., Bhalla, S., Berwick, S., Christian, K., Mascarenhas, J., Dembitzer, F., Moshier, E., & Tremblay, D. (2020). Admission D-dimer levels, D-dimer trends, and outcomes in COVID-19. Thrombosis Research, 196(June), 99–105. doi: 10.1016/j.thromres.2020.08.032. Nazir, A., & Ampadu, H. K. (2022). Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients. PeerJ Computer Science, 8 (January 2020), e889. NHS (2022). AI Skunkworks projects. Available at: https://transform.england.nhs.uk/ai-la b/ai-lab-programmes/skunkworks/ai-skunkworks-projects/ (Accessed: 12-10-2022). Nijman, S., Leeuwenberg, A., Beekers, I., Verkouter, I., Jacobs, J., Bots, M., Asselbergs, F., Moons, K., & Debray, T. (2022). Missing data is poorly handled and reported in prediction model studies using machine learning: A literature review. Journal of Clinical Epidemiology, 142, 218–229. https://doi.org/10.1016/j.jclinepi.2021.11.023 Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., … Wiegand, T. (2021). Machine learning for health: Algorithm auditing & quality control. Journal of Medical Systems, 45(12). https://doi.org/10.1007/s10916-021- 01783-y Ordu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2021). A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach. Journal of the Operational Research Society, 72(3), 485–500. https://doi.org/10.1080/ 01605682.2019.1700186 Ortíz-Barrios, M. A., & Alfaro-Saíz, J. (2020). Methodological approaches to support process improvement in emergency departments: A systematic review. International Journal of Environmental Research and Public Health, 17(8). https://doi.org/10.3390/ ijerph17082664 Ortiz-Barrios, M., & Alfaro-Saiz, J. (2020). An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector. PLoS ONE, 15(6 June). doi: 10.1371/journal.pone.0234984. Ortíz-Barrios, M. A., Coba-Blanco, D. M., Alfaro-Saíz, J. J., & Stand-Gonz´ alez, D. (2021). Process improvement approaches for increasing the response of emergency departments against the COVID-19 pandemic: A systematic review. International Journal of Environmental Research and Public Health, 18(16), 8814. https://doi.org/ 10.3390/ijerph18168814 Patel, D., Kher, V., Desai, B., Lei, X., Cen, S., Nanda, N., Gholamrezanezhad, A., Duddalwar, V., Varghese, B., & Oberai, A. A. (2021). Machine learning based predictors for COVID-19 disease severity. Scientific Reports, 11(1), 1–7. https://doi. org/10.1038/s41598-021-83967-7 Petrilli, C. M., Jones, S. A., Yang, J., Rajagopalan, H., O’Donnell, L., Chernyak, Y., … Horwitz, L. I. (2020). Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: Prospective cohort study. BMJ, 369. https://doi.org/10.1136/bmj.m1966 Pezoulas, V. C., Kourou, K. D., Mylona, E., Papaloukas, C., Liontos, A., Biros, D., Milionis, O. I., Kyriakopoulos, C., Kostikas, K., Milionis, H., & Fotiadis, D. I. (2022). ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Computers in Biology and Medicine, 141(November 2021), Article 105176. https://doi.org/10.1016/j. compbiomed.2021.105176 Piccialli, F., di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23(6), 1467–1497. https://doi.org/10.1007/s10796-021-10131-x Possik, J., Asgary, A., Solis, A. O., Zacharewicz, G., Shafiee, M. A., Najafabadi, M. M., … Wu, J. (2022). An agent-based modeling and virtual reality application using distributed simulation: Case of a COVID-19 intensive care unit. IEEE Transactions on Engineering Management, 1–13. https://doi.org/10.1109/TEM.2022.3195813 Pourhomayoun, M., & Shakibi, M. (2021). Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health, 20, Article 100178. https://doi.org/10.1016/j.smhl.2020.100178 Privett, N., & Gonsalvez, D. (2014). The top ten global health supply chain issues: Perspectives from the field. Operations Research for Health Care, 3(4), 226–230. Rahman, N. A. A., Ahmi, A., Jraisat, L., & Upadhyay, A. (2022). Examining the trend of humanitarian supply chain studies: Pre, during and post COVID-19 pandemic. Journal of Humanitarian Logistics and Supply Chain Management (ahead-of-print). Raj, A., Mukherjee, A. A., de Sousa Jabbour, A. B. L., & Srivastava, S. K. (2022). Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. Journal of Business Research, 142, 1125–1139. Rees, E. M., Nightingale, E. S., Jafari, Y., Waterlow, N. R., Clifford, S., Carl, C. A., Group, C. W., Jombart, T., Procter, S. R., & Knight, G. M. (2020). COVID-19 length of hospital stay: A systematic review and data synthesis. BMC Medicine, 18(1). https:// doi.org/10.1186/s12916-020-01726-3 Restrepo, M. I., Rousseau, L. M., & Vall´ee, J. (2020). Home healthcare integrated staffing and scheduling. Omega, 95, Article 102057. Robinson, S. (2002). General concepts of quality for discrete-event simulation. European Journal of Operational Research, 138(1), 103–117. Robinson, S. (2005). Discrete-event simulation: From the pioneers to the present, what next? Journal of the Operational Research Society, 56(6), 619–629. Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018). The future and social impact of big data analytics in supply chain management: Results from a Delphi study. Technological Forecasting and Social Change, 130, 135–149. https://doi. org/10.1016/j.techfore.2017.10.005 Rostami, M., & Mansouritorghabeh, H. (2020). D-dimer level in COVID-19 infection: A systematic review. Expert Review of Hematology, 13(11), 1265–1275. https://doi.org/ 10.1080/17474086.2020.1831383 Sage Growth Report (2021). The state of healthcare automation. Available at: http://go. sage-growth.com/healthcare-automation-success-030921-ms (Accessed: 10-10- 2022). Sala, F., Quarto, M., & D’urso, G. (2022). Simulation study of the impact of COVID-19 policies on the efficiency of a smart clinic MRI service. Healthcare (Switzerland), 10 (4) doi: 10.3390/healthcare10040619. Sawangarreerak, S., & Thanathamathee, P. (2020). Random forest with sampling techniques for handling imbalanced prediction of university student depression. Information, 11(11), 1–13. https://doi.org/10.3390/info11110519 Schwab, P., Schütte, A. D. M., Dietz, B., & Bauer, S. (2020). Clinical predictive models for COVID-19: Systematic study. Journal of Medical Internet Research, 22(10). https:// doi.org/10.2196/21439 Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23. Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022). Using decision tree algorithms for estimating ICU admission of COVID-19 patients. Informatics in Medicine Unlocked, 30(February), Article 100919. https://doi.org/10.1016/j. imu.2022.100919 Sharma, M., Kumar, A., Luthra, S., Joshi, S., & Upadhyay, A. (2022). The impact of environmental dynamism on low-carbon practices and digital supply chain networks to enhance sustainable performance: An empirical analysis. Business Strategy and the Environment. Sharma, G., Volgman, A. S., & Michos, E. D. (2020). Sex differences in mortality from COVID-19 pandemic: Are men vulnerable and women protected? JACC: Case Reports, 2(9), 1407–1410. https://doi.org/10.1016/j.jaccas.2020.04.027 Sheng, J., Amankwah-Amoah, J., Khan, Z., & Wang, X. (2021). COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164–1183. https://doi.org/ 10.1111/1467-8551.12441 Simsekler, M. C. E., Alhashmi, N. H., Azar, E., King, N., Luqman, R. A. M. A., & Al Mulla, A. (2021). Exploring drivers of patient satisfaction using a random forest algorithm. BMC Medical Informatics and Decision Making, 21(1), 1–9. https://doi.org/ 10.1186/s12911-021-01519-5 Simsekler, M. C. E., Qazi, A., Alalami, M. A., Ellahham, S., & Ozonoff, A. (2020). Evaluation of patient safety culture using a random forest algorithm. Reliability Engineering and System Safety, 204(April), Article 107186. https://doi.org/10.1016/j. ress.2020.107186 Sitepu, S., Mawengkang, H., & Husein, I. (2018). Optimization model for capacity management and bed scheduling for hospital. In IOP conference series: Materials science and engineering (Vol. 300, No. 1, p. 012016). IOP Publishing. Soni, M., Gopalakrishnan, R., Vaishya, R., & Prabu, P. (2020). D-dimer level is a useful predictor for mortality in patients with COVID-19: Analysis of 483 cases. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(6), 2245–2249. https:// doi.org/10.1016/j.dsx.2020.11.007 Srivastava, D. K., Kumar, V., Ekren, B. Y., Upadhyay, A., Tyagi, M., & Kumari, A. (2022). Adopting Industry 4.0 by leveraging organisational factors. Technological Forecasting and Social Change, 176, Article 121439. Standfield, L., Comans, T., & Scuffham, P. (2014). Markov modeling and discrete event simulation in health care: A systematic comparison. International Journal of Technology Assessment in Health Care, 30(2), 165–172. St ˇ ˇep´ ankov´ a, O., Aubrecht, P., Kouba, Z., & Mikˇsovský, P. (2003). Preprocessing for data mining and decision support. In Data mining and decision support (pp. 107–117). Boston, MA: Springer. https://doi.org/10.1007/978-1-4615-0286-9_9. Sujatha, R., Venkata Siva Krishna, B., Chatterjee, J. M., Naidu, P. R., Jhanjhi, N. Z., Charita, C., Mariya, E. N., & Baz, M. (2022). Prediction of suitable candidates for covid-19 vaccination. Intelligent Automation and Soft Computing, 32(1), 525–541. doi: 10.32604/iasc.2022.021216. Sumari, S., Ibrahim, R., Zakaria, N. H., & Ab Hamid, A. H. (2013). Comparing three simulation model using taxonomy: System dynamic simulation, discrete event simulation and agent based simulation. International Journal of Management Excellence, 1(3), 54–59. Sun, T. Q. (2021). Adopting artificial intelligence in public healthcare: The effect of social power and learning algorithms. International Journal of Environmental Research and Public Health, 18(23). https://doi.org/10.3390/ijerph182312682 Sun, L., Song, F., Shi, N., Liu, F., Li, S., Li, P., … Shi, Y. (2020). Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID19. Journal of Clinical Virology, 128, Article 104431. https://doi.org/10.1016/j. jcv.2020.104431 Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: A real-case study. Medical & Biological Engineering & Computing, 1–22. https://doi.org/10.1007/s11517-022- 02525-z Thakur, R., Hsu, S. H. Y., & Fontenot, G. (2012). Innovation in healthcare: Issues and future trends. Journal of Business Research, 65(4), 562–569. https://doi.org/ 10.1016/j.jbusres.2011.02.022 Tharakan, S., Nomoto, K., Miyashita, S., & Ishikawa, K. (2020). Body temperature correlates with mortality in COVID-19 patients. Critical Care, 24(1). https://doi.org/ 10.1186/s13054-020-03045-8 Upadhyay, A., Hernandez, M. J. P., & Balodi, K. C. (2022). Covid-19 Disaster relief projects management: An exploratory study of critical success factors. Operations Management Research, 1–12. Vazquez-Serrano, ´ J. I., Peimbert-García, R. E., & C´ ardenas-Barron, ´ L. E. (2021). Discreteevent simulation modeling in healthcare: A comprehensive review. International Journal of Environmental Research and Public Health, 18(22). https://doi.org/ 10.3390/ijerph182212262 Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., … Ferguson, N. M. (2020). Estimates of the severity of coronavirus disease 2019: A model-based analysis. The Lancet Infectious Diseases, 20(6), 669–677. https://doi. org/10.1016/S1473-3099(20)30243-7 Vickers, A. J., Cronin, A. M., & Begg, C. B. (2011). One statistical test is sufficient for assessing new predictive markers. BMC Medical Research Methodology, 11(1), 1–7. https://doi.org/10.1186/1471-2288-11-13 Vijiyakumar, K., Lavanya, B., Nirmala, I., & Sofia Caroline, S. (2019). Random forest algorithm for the prediction of diabetes. In Paper presented at the 2019 IEEE international conference on system, computation, automation and networking. https:// doi.org/10.1109/ICSCAN.2019.8878802 Wang, R. Y., Guo, T. Q., Li, L. G., Jiao, J. Y., & Wang, L. Y. (2020). Predictions of COVID19 infection severity based on co-associations between the SNPs of co-morbid diseases and COVID-19 through machine learning of genetic data. In 2020 IEEE 8th International conference on computer science and network technology (ICCSNT) (pp. 92–96). IEEE. doi: 10.1109/ICCSNT50940.2020.9304990. Wang, Z., Upadhyay, A., & Kumar, A. (2022). A real options approach to growth opportunities and resilience aftermath of the COVID-19 pandemic. Journal of Modelling in Management (ahead-of-print). Wang, K., Gheblawi, M., & Oudit, G. Y. (2020). Angiotensin converting enzyme 2: A double-edged sword. Circulation, 142(5), 426–428. https://doi.org/10.1161/ CIRCULATIONAHA.120.047049 Wang, T., Tang, R., Ruan, H., Chen, R., Zhang, Z., Sang, L., & China Medical Treatment Expert Group for COVID-19. (2021). Predictors of fatal outcomes among hospitalized COVID-19 patients with pre-existing hypertension in china. Clinical Respiratory Journal, 15(8), 915–924. https://doi.org/10.1111/crj.13382 Wang, Y., Wang, Z., Tse, G., Zhang, L., Wan, E. Y., Guo, Y., Lip, G. Y. H., Li, G., Lu, Z., & Liu, T. (2020). Cardiac arrhythmias in patients with COVID-19. Journal of Arrhythmia, 36(5), 827–836. https://doi.org/10.1002/joa3.12405 Whitworth, J. (2020). COVID-19: A fast evolving pandemic. Transactions of the Royal Society of Tropical Medicine and Hygiene, 114(4), 227–228. https://doi.org/10.1093/ trstmh/traa025 Wood, R. M., McWilliams, C. J., Thomas, M. J., Bourdeaux, C. P., & Vasilakis, C. (2020). COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care. Health Care Management Science, 23(3), 315–324. https://doi.org/ 10.1007/s10729-020-09511-7 Xie, Q., Chen, Y., Hu, Y., Zeng, F., Wang, P., Xu, L., … Zeng, F. (2022). Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Medical Imaging, 22(1). https://doi.org/10.1186/s12880-022-00868-5 Yang, P., Yang, G., Qi, J., Sheng, B., Yang, Y., Zhang, S., … Mao, X. (2021). The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: A modelling study. Scientific Reports, 11(1), 1–13. https://doi.org/ 10.1038/s41598-021-82170-y Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305- z Yusriza, F. A., Abdul Rahman, N. A., Jraisat, L., & Upadhyay, A. (2022). Airline catering supply chain performance during pandemic disruption: A bayesian network modelling approach. International Journal of Quality and Reliability Management. https://doi.org/10.1108/IJQRM-01-2022-0027 Zhang, X. (2018). Application of discrete event simulation in health care: A systematic review. BMC Health Services Research, 18(1), 1–11. https://doi.org/10.1186/s12913- 018-3456-4 Zhang, L., Yan, X., Fan, Q., Liu, H., Liu, X., Liu, Z., & Zhang, Z. (2020). D-dimer levels on admission to predict in-hospital mortality in patients with covid-19. Journal of Thrombosis and Haemostasis, 18(6), 1324–1329. https://doi.org/10.1111/jth.14859 Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., & Cao, B. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. The Lancet, 395(10229), 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3 Zhu, Z., Hen, B. H., & Teow, K. L. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance, 25(2), 134–144. https://doi.org/10.1108/09526861211198290
dc.relation.citationendpage.spa.fl_str_mv	22
dc.relation.citationstartpage.spa.fl_str_mv	1
dc.relation.citationvolume.spa.fl_str_mv	160
dc.rights.eng.fl_str_mv	© 2023 Elsevier Inc. All rights reserved.
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.uri.spa.fl_str_mv	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/embargoedAccess
dc.rights.coar.spa.fl_str_mv	http://purl.org/coar/access_right/c_f1cf
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) © 2023 Elsevier Inc. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_f1cf
eu_rights_str_mv	embargoedAccess
dc.format.extent.spa.fl_str_mv	22 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Elsevier Inc.
dc.publisher.place.spa.fl_str_mv	United States
dc.source.spa.fl_str_mv	https://www.sciencedirect.com/science/article/pii/S0148296323001649
institution	Corporación Universidad de la Costa
bitstream.url.fl_str_mv	https://repositorio.cuc.edu.co/bitstreams/acdd4c09-fe86-4f8a-86fa-94593ab2668a/download https://repositorio.cuc.edu.co/bitstreams/900c09af-e2bd-42f4-926f-c61bc64870fd/download https://repositorio.cuc.edu.co/bitstreams/315982d6-def1-48a0-8874-6905c0af761b/download https://repositorio.cuc.edu.co/bitstreams/9eecc2c7-2993-4e14-860a-ab0a96e62dae/download
bitstream.checksum.fl_str_mv	b09aef9d9b9c79f6d9c545b6aaa961bd 2f9959eaf5b71fae44bbf9ec84150c7a 8158cc2a921c2444a51cef73c85875bc 0da299db99ad838a23bccfe48a8496c4
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv	repdigital@cuc.edu.co
_version_	1811760762983546880
spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023 Elsevier Inc. All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfOrtiz Barrios, Miguel AngelArias Fonseca, Sebastian Ishizaka, AlessioBarbati, MariaAvendano-Collante, BettyNavarro Jiménez, Eduardo2023-09-06T14:09:32Z20262023-09-06T14:09:32Z2023Miguel Ortiz-Barrios, Sebastián Arias-Fonseca, Alessio Ishizaka, Maria Barbati, Betty Avendaño-Collante, Eduardo Navarro-Jiménez, Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study, Journal of Business Research, Volume 160, 2023, 113806, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2023.1138060148-2963https://hdl.handle.net/11323/1044710.1016/j.jbusres.2023.1138061873-7978Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.22 páginasapplication/pdfengElsevier Inc.United Stateshttps://www.sciencedirect.com/science/article/pii/S0148296323001649Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case studyArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Journal of Business ResearchAbdalkareem, Z. A., Amir, A., Al-Betar, M. A., Ekhan, P., & Hammouri, A. I. (2021). Healthcare scheduling in optimization context: A review. Health and Technology, 11 (3), 445–469.Agrawal, R., Wankhede, V. A., Kumar, A., Upadhyay, A., & Garza-Reyes, J. A. (2021). Nexus of circular economy and sustainable business performance in the era of digitalization. International Journal of Productivity and Performance Management, 71 (3), 748–774.Ahmad, J., Iqbal, J., Ahmad, I., Khan, Z. A., Tiwana, M. I., & Khan, K. (2020). A simulation based study for managing hospital resources by reducing patient waiting time. IEEE Access, 8, 193523–193531. https://doi.org/10.1109/ ACCESS.2020.3032760Akhavan, A. R., Habboushe, J. P., Gulati, R., Iheagwara, O., Watterson, J., Thomas, S., … Lee, D. C. (2020). Risk stratification of covid-19 patients using ambulatory oxygen saturation in the emergency department. Western Journal of Emergency Medicine, 21 (6). https://doi.org/10.5811/WESTJEM.2020.8.48701Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons and Fractals, 140. https://doi.org/10.1016/j. chaos.2020.110120Alballa, N., & Al-Turaiki, I. (2021). Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked, 24, Article 100564. https://doi.org/10.1016/j.imu.2021.100564Albitar, O., Ballouze, R., Ooi, J. P., & Sheikh Ghadzi, S. M. (2020). Risk factors for mortality among COVID-19 patients. Diabetes Research and Clinical Practice, 166, Article 108293. https://doi.org/10.1016/j.diabres.2020.108293Almeida, A., & Vales, J. (2020). The impact of primary health care reform on hospital emergency department overcrowding: Evidence from the portuguese reform. International Journal of Health Planning and Management, 35(1), 368–377. https://doi. org/10.1002/hpm.2939Aly, M. H., Rahman, S. S., Ahmed, W. A., Alghamedi, M. H., Al Shehri, A. A., Alkalkami, A. M., & Hassan, M. H. (2020). Indicators of critical illness and predictors of mortality in COVID-19 patients. Infection and Drug Resistance, 13, 1995–2000. https://doi.org/10.2147/IDR.S261159Amantea, I. A., Di Leva, A., & Sulis, E. (2020). A simulation-driven approach to decision support in process reorganization: A case study in healthcare. Lecture Notes in Information Systems and Organisation, 33, 223–235. https://doi.org/10.1007/978-3- 030-23665-6_16Assaf, D., Gutman, Y., Neuman, Y., Segal, G., Amit, S., Gefen-Halevi, S., … Tirosh, A. (2020). Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Internal and Emergency Medicine, 15(8), 1435–1443. https://doi. org/10.1007/s11739-020-02475-0Aznar-Gimeno, R., Esteban, L. M., Labata-Lezaun, G., Del-Hoyo-alonso, R., AbadiaGallego, D., Pano-Pardo, ˜ J. R., Esquillor-Rodrigo, M. J., Lanas, A., ´ & Serrano, M. T. (2021). A clinical decision web to predict ICU admission or death for patients hospitalised with COVID-19 using machine learning algorithms. International Journal of Environmental Research and Public Health, 18(16). https://doi.org/10.3390/ ijerph18168677Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., … Mackenzie, L. S. (2020). Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. International Immunopharmacology, 86, Article 106705. https://doi.org/10.1016/j. intimp.2020.106705Barrios, M. A. O., Caballero, J. E., & Sanchez, ´ F. S. (2015). A methodology for the creation of integrated service networks in outpatient internal medicine. doi: 10.1007/978-3-319- 26508-7_24.Batista, G. E. A. P. A., & Monard, M. C. (2003). An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5–6), 519–533. https://doi.org/10.1080/713827181Behl, A., Gaur, J., Pereira, V., Yadav, R., & Laker, B. (2022). Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19–A multi-theoretical approach. Journal of Business Research, 148, 378–389.Belgiu, M., & Dragut ˘ ¸, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.Bihri, H., Hsaini, S., Nejjari, R., Azzouzi, S., & Charaf M.E.H. (2022). Missing data analysis in the healthcare field: COVID-19 case study. In Networking, intelligent systems and security. Smart innovation, systems and technologies (Vol. 237). Ssingapore: Springer. doi: 10.1007/978-981-16-3637-0_61.Birkmeyer, J. D., Barnato, A., Birkmeyer, N., Bessler, R., & Skinner, J. (2020). The impact of the COVID-19 pandemic on hospital admissions in the United States: Study examines trends in US hospital admissions during the COVID-19 pandemic. Health Affairs, 39(11), 2010–2017. https://doi.org/10.1377/hlthaff.2020.00980Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial intelligence in healthcare (pp. 25–60). Academic Press.Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/ 10.1023/A:1010933404324Brennan, A., Chick, S. E., & Davies, R. (2006). A taxonomy of model structures for economic evaluation of health technologies. Health economics, 15(12), 1295–1310.Caillon, A., Zhao, K., Klein, K. O., Greenwood, C. M. T., Lu, Z., Paradis, P., & Schiffrin, E. L. (2021). High systolic blood pressure at hospital admission is an important risk factor in models predicting outcome of COVID-19 patients. American Journal of Hypertension, 34(3), 282–290. https://doi.org/10.1093/ajh/hpaa225Caro, J. J., Moller, ¨ J., & Getsios, D. (2010). Discrete event simulation: The preferred technique for Health Economic Evaluations? Value in Health, 13(8), 1056–1060. https://doi.org/10.1111/j.1524-4733.2010.00775.xCaro, J. J., Moller, ¨ J., Santhirapala, V., Gill, H., Johnston, J., El-Boghdadly, K., Santhirapala, R., Kelly, P., & McGuire, A. (2021). Predicting hospital resource use during COVID-19 surges: A simple but flexible discretely integrated condition event simulation of individual patient-hospital trajectories. Value in Health, 24(11), 1570–1577. https://doi.org/10.1016/j.jval.2021.05.023Casier, G., Casier, K., Van Ooteghem, J., & Verbrugge, S. (2015). Application of a discrete event simulator for healthcare processes. In BMSD 2015 - Proceedings of the 5th international symposium on business modeling and software design. https://doi.org/ 10.5220/0005887702410246Casiraghi, E., Malchiodi, D., Trucco, G., Frasca, M., Cappelletti, L., Fontana, T., … Valentini, G. (2020). Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments. IEEE Access, 8, 196299–196325. https:// doi.org/10.1109/ACCESS.2020.3034032Castanheira-Pinto, A., Gonçalves, B. S., Lima, R. M., & Dinis-Carvalho, J. (2021). Modeling, assessment and design of an emergency department of a public hospital through discrete-event simulation. Applied Sciences (Switzerland), 11(2), 1–25. https://doi.org/10.3390/app11020805Chakravorty, T., Jha, K., & Barthwal, S. (2018). Digital technologies as enablers of carequality and performance: A conceptual review of hospital supply chain network. IUP Journal of Supply Chain Management, 15(3), 7–25.Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2021). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research, 1–26.Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential guide for health leaders. In Healthcare management forum (Vol. 33, No. 1, pp. 10–18). Los Angeles, CA: SAGE Publications.Cheng, F. Y., Joshi, H., Tandon, P., Freeman, R., Reich, D. L., Mazumdar, M., Kohli-seth, R., Levin, M., Timsina, P., & Kia, A. (2020). Using machine learning to predict ICU transfer in hospitalized COVID-19 patients. Journal of Clinical Medicine, 9(6). doi: 10.3390/jcm9061668.Cheng, A., Hu, L., Wang, Y., Huang, L., Zhao, L., Zhang, C., … Liu, Q. (2020). Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. International Journal of Antimicrobial Agents, 56(3). https://doi.org/10.1016/j.ijantimicag.2020.106110Choi, Y., Lee, H., & Irani, Z. (2018). Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Annals of Operations Research, 270(1–2), 75–104. https://doi.org/10.1007/s10479-016-2281-6Choron, R. L., Butts, C. A., Bargoud, C., Krumrei, N. J., Teichman, A. L., Schroeder, M. E., Bover Manderski, M. T., Cai, J., Song, C., Rodricks, M. B., Lissauer, M., & Gupta, R. (2021). Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID19 Patients. Journal of Intensive Care Medicine, 36(4), 484–493. https://doi.org/ 10.1177/0885066620979622Chowdhury, M., Cervantes, E. G., Chan, W. Y., & Seitz, D. P. (2021). Use of machine learning and artificial intelligence methods in geriatric mental health research involving electronic health record or administrative claims data: A systematic review. Frontiers Psychiatry, 12(September). https://doi.org/10.3389/ fpsyt.2021.738466Corro Ramos, I., Hoogendoorn, M., & Rutten-van Molken, ¨ M. P. M. H. (2020). How to address uncertainty in health economic discrete-event simulation models: An illustration for chronic obstructive pulmonary disease. Medical Decision Making, 40 (5), 619–632. https://doi.org/10.1177/0272989X20932145Currie, C. S. M., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID19. Journal of Simulation, 14(2), 83–97. https://doi.org/10.1080/ 17477778.2020.1751570Davis, Z., Zobel, C. W., Khansa, L., & Glick, R. E. (2020). Emergency department resilience to disaster-level overcrowding: A component resilience framework for analysis and predictive modeling. Journal of Operations Management, 66(1–2), 54–66. https://doi.org/10.1002/joom.1017De Santis, A., Giovannelli, T., Lucidi, S., Messedaglia, M., & Roma, M. (2021). Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of emergency department patient arrivals. Flexible Services and Manufacturing Journal, 1–34. https://doi.org/10.1007/s10696-021-09408-9Dergaa, I., Abubaker, M., Souissi, A., Mohammed, A. R., Varma, A., Musa, S., Al Naama, A., Mkaouer, B., & Saad, H. B. (2022). Age and clinical signs as predictors of COVID19 symptoms and cycle threshold value. Libyan Journal of Medicine, 17(1). doi: 10.1080/19932820.2021.2010337.Di Castelnuovo, A., Bonaccio, M., Costanzo, S., Gialluisi, A., Antinori, A., Berselli, N., … COVID, T. (2020). Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: Survival analysis and machine learning-based findings from the multicentre Italian CORIST study. Nutrition, Metabolism and Cardiovascular Diseases, 30(11), 1899–1913. doi:10.1016/j.numecd.2020.07.031.Doneda, M., Yalçındag, ˘ S., Marques, I., & Lanzarone, E. (2021). A discrete-event simulation model for analysing and improving operations in a blood donation centre. Vox Sanguinis, 116(10), 1060–1075. https://doi.org/10.1111/vox.13111Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284–289. https://doi.org/10.1016/j. jbusres.2020.06.008Drewry, A. M., Hotchkiss, R., & Kulstad, E. (2020). Response to “body temperature correlates with mortality in COVID-19 patients”. Critical Care, 24(1). https://doi. org/10.1186/s13054-020-03186-wEllahham, S., Ellahham, N., & Simsekler, M. C. E. (2020). Application of artificial intelligence in the health care safety context: Opportunities and challenges. American Journal of Medical Quality, 35(4), 341–348. https://doi.org/10.1177/ 1062860619878515Famiglini, L., Campagner, A., Carobene, A., & Cabitza, F. (2022). A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients. Medical and Biological Engineering and Computing. , Article 0123456789. https://doi.org/10.1007/s11517-022-02543-xFawagreh, K., & Gaber, M. M. (2020). Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach. Computing, 102(5), 1187–1198.Feng, C., Kephart, G., & Juarez-Colunga, E. (2021). Predicting COVID-19 mortality risk in Toronto, Canada: A comparison of tree-based and regression-based machine learning methods. BMC Medical Research Methodology, 21(1), 1–14. https://doi.org/10.1186/ s12874-021-01441-4Fernandes, F. T., de Oliveira, T. A., Teixeira, C. E., Batista, A. F. de M., Dalla Costa, G., & Chiavegatto Filho, A. D. P. (2021). A multipurpose machine learning approach to predict COVID-19 negative prognosis in S˜ ao Paulo, Brazil. Scientific Reports, 11(1), 1–7. doi: 10.1038/s41598-021-82885-y.Figliozzi, S., Masci, P. G., Ahmadi, N., Tondi, L., Koutli, E., Aimo, A., … Georgiopoulos, G. (2020). Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis. European Journal of Clinical Investigation, 50(10). https:// doi.org/10.1111/eci.13362Forbes (2022). AI for health and hope: How machine learning is being used in hospitals. Available at: https://www.forbes.com/sites/forbestechcouncil/2022/02/16/ai -for-health-and-hope-how-machine-learning-is-being-used-in-hospitals/ (Accessed: 10-10-2022).Frid-Adar, M., Amer, R., Gozes, O., Nassar, J., & Greenspan, H. (2021). COVID-19 in CXR: From detection and severity scoring to patient disease monitoring. IEEE Journal of Biomedical and Health Informatics, 25(6), 1892–1903. https://doi.org/10.1109/ JBHI.2021.3069169Gallo Marin, B., Aghagoli, G., Lavine, K., Yang, L., Siff, E. J., Chiang, S. S., … Michelow, I. C. (2021). Predictors of COVID-19 severity: A literature review. Reviews in Medical Virology, 31(1), 1–10. https://doi.org/10.1002/rmv.2146Garcia-Vicuna, ˜ D., Esparza, L., & Mallor, F. (2021). Hospital preparedness during epidemics using simulation: The case of COVID-19. Central European Journal of Operations Research. https://doi.org/10.1007/s10100-021-00779-wGarcia-Vicuna, D., Mallor, F., & Esparza, L. (2020). Planning ward and intensive care unit beds for COVID-19 patients using a discrete event simulation model. In Paper presented at the proceedings - winter simulation conference, 2020-December (pp. 759–770). doi: 10.1109/WSC48552.2020.9383939.Gillespie, J., McClean, S., Garg, L., Barton, M., Scotney, B., & Fullerton, K. (2016). A multi-phase DES modelling framework for patient-centred care. Journal of the Operational Research Society, 67(10), 1239–1249. https://doi.org/10.1057/ jors.2015.114Gok, ¨ E. C., & Olgun, M. O. (2021). SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples. Neural Computing and Applications, 33(22), 15693–15707. https://doi.org/ 10.1007/s00521-021-06189-yGopinath, N. (2021). Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic. Process Biochemistry, 110(August), 94–99. https://doi.org/10.1016/j. procbio.2021.08.001Gorunescu, F., McClean, S. I., & Millard, P. H. (2002). A queueing model for bedoccupancy management and planning of hospitals. Journal of the Operational Research Society, 53(1), 19–24.Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, Article 101967.Griffin, D. O., Griffin, D. O., Jensen, A., Khan, M., Chin, J., Chin, K., … Patel, D. (2020). Pulmonary embolism and increased levels of D-dimer in patients with coronavirus disease. Emerging Infectious Diseases, 26(8), 1941–1943. https://doi.org/10.3201/ eid2608.201477Gul, M., & Guneri, A. F. (2012). A computer simulation model to reduce patient length of stay and to improve resource utilization rate in an emergency department service system. International Journal of Industrial Engineering, 19(5), 221–231. 10.23055/ij ietap.2012.19.5.793.Gumaei, A., Ismail, W. N., Rafiul Hassan, M., Hassan, M. M., Mohamed, E., Alelaiwi, A., & Fortino, G. (2022). A decision-level fusion method for COVID-19 patient health prediction. Big Data Research, 27, Article 100287. https://doi.org/10.1016/j. bdr.2021.100287Günal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation, 4, 42–51. https://doi. org/10.1057/jos.2009.25Guo, T., Fan, Y., Chen, M., Xiaoyan, W.u., Zhang, L., He, T., Wang, H., Wan, J., Wang, X., & Zhibing, L.u. (2020). Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA Cardiology, 5(7), 811–888. https://doi.org/10.1001/jamacardio.2020.1017Gupta, V. K., Gupta, A., Kumar, D., & Sardana, A. (2021). Prediction of COVID-19 confirmed, death, and cured cases in india using random forest model. Big Data Mining and Analytics, 4(2), 116–123. https://doi.org/10.26599/ BDMA.2020.9020016Heineke, J. (1995). Strategic operations management decisions and professional performance in US HMOs. Journal of Operations Management, 13(4), 255–272.Heldt, F. S., Vizcaychipi, M. P., Peacock, S., Cinelli, M., McLachlan, L., Andreotti, F., … Khan, R. T. (2021). Early risk assessment for COVID-19 patients from emergency department data using machine learning. Scientific Reports, 11(1), 1–13. doi: 10.1038/s41598-021-83784-y.Hosmer, D. W. Jr., Lemeshow, S., Sturdivant, R.X. (2013). Applied logistic regression (Vol. 398). New York, NY: John Wiley & Sons.Hou, W., Zhao, Z., Chen, A., Li, H., & Duong, T. Q. (2021). Machining learning predicts the need for escalated care and mortality in covid-19 patients from clinical variables. International Journal of Medical Sciences, 18(8), 1739–1745. doi: 10.7150/ ijms.51235.Hsu, J., Hung, P., Lin, H., & Hsieh, C. (2015). Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer. Journal of Medical Systems, 39(4) doi: 10.1007/s10916-015-0210-x.Hu, J., Zhou, J., Dong, F., Tan, J., Wang, S., Li, Z., … Huang, T. (2020). Combination of serum lactate dehydrogenase and sex is predictive of severe disease in patients with COVID-19. Medicine, 99(42), e22774.Huang, H. F., Liu, Y., Li, J. X., Dong, H., Gao, S., Huang, Z. Y., Fu, S. Z., Yang, L. Y., Lu, H. Z., Xia, L. Y., Cao, S., Gao, Y., & Yu, X. X. (2021). Validated tool for early prediction of intensive care unit admission in COVID-19 patients. World Journal of Clinical Cases, 9(28), 8388–8403. 10.12998/wjcc.v9.i28.8388.Huang, S., Yang, J., Fong, S., & Zhao, Q. (2021). Artificial intelligence in the diagnosis of COVID-19: Challenges and perspectives. International Journal of Biological Sciences, 17(6), 1581. https://doi.org/10.7150/ijbs.58855Ikemura, K., Bellin, E., Yagi, Y., Billett, H., Saada, M., Simone, K., … Gil, M. R. (2021). Using automated machine learning to predict the mortality of patients with COVID19: Prediction model development study. Journal of Medical Internet Research, 23(2). https://doi.org/10.2196/23458Irvine, N., Anderson, G., Sinha, C., McCabe, H., & Van der Meer, R. (2021). Collaborative critical care prediction and resource planning during the COVID-19 pandemic using computer simulation modelling: Future urgent planning lessons. Future Healthcare Journal, 8(2), e317–e321. https://doi.org/10.7861/fhj.2020-0194Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of Big Data, 7(1), 1–22.Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/ 00207543.2018.1488086Iwendi, C., Bashir, A. K., Peshkar, A., Sujatha, R., Chatterjee, J. M., Pasupuleti, S., Mishra, R., Pillai, S., & Jo, O. (2020). COVID-19 patient health prediction using boosted random forest algorithm. Frontiers in Public Health, 8(July), 1–9. https://doi. org/10.3389/fpubh.2020.00357Iyengar, K. P., Vaishya, R., Bahl, S., & Vaish, A. (2020). Impact of the coronavirus pandemic on the supply chain in healthcare. British Journal of Healthcare Management, 26(6), 1–4.Jack, E. P., & Powers, T. L. (2009). A review and synthesis of demand management, capacity management and performance in health-care services. International Journal of Management Reviews, 11, 149–174. https://doi.org/10.1111/j.1468- 2370.2008.00235.xJadhav, S., Kasar, R., Lade, N., Patil, M., & Kolte, S. (2017). Disease prediction by machine learning from healthcare communities. International Journal of Scientific Research in Science and Technology, 29–35. 10.32628/ijsrst19633.Jarrett, M., Schultz, S., Lyall, J., Wang, J., Stier, L., de Geronimo, M., & Nelson, K. (2020). Clinical mortality in a large COVID-19 cohort: Observational study. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/23565Jraisat, L., Jreissat, M., Upadhyay, A., & Kumar, A. (2022). Blockchain technology: The role of integrated reverse supply chain networks in sustainability. In Supply chain forum: An international journal (pp. 1–14). Taylor & Francis.Jun, J. B., Jacobson, S. H., & Swisher, J. R. (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society, 50(2), 109–123. https://doi.org/10.1057/palgrave.jors.2600669Kabir, G., Tesfamariam, S., Hemsing, J., & Sadiq, R. (2020). Handling incomplete and missing data in water network database using imputation methods. Sustainable and Resilient Infrastructure, 5(6), 365–377. https://doi.org/10.1080/ 23789689.2019.1600960Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Systems, 38 (9), 1–20.Khalilia, M., Chakraborty, S., & Popescu, M. (2011). Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making, 11(1), 1–13.Kim, J., Lim, H., Ahn, J. H., Lee, K. H., Lee, K. S., & Koo, K. C. (2021). Optimal triage for COVID-19 patients under limited health care resources with a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation: Development study. JMIR Medical Informatics, 9(11), e32726.Klassen, K. J., & Rohleder, T. R. (2001). Combining operations and marketing to manage capacity and demand in services. Service Industries Journal, 21(2), 1–30.Kochan, C., Nowicki, D. R., Sauser, B., & Randall, W. S. (2018). Impact of cloud-based information sharing on hospital supply chain performance: A system dynamics framework. International Journal of Production Economics, 195, 168–185. https://doi. org/10.1016/j.ijpe.2017.10.008Kortbeek, N., Braaksma, A., Smeenk, F. H., Bakker, P. J., & Boucherie, R. J. (2015). Integral resource capacity planning for inpatient care services based on bed census predictions by hour. Journal of the Operational Research Society, 66(7), 1061–1076.Kumar, A., & Mo, J. (2010). Models for bed occupancy management of a hospital in Singapore. In Proceedings of the 2010 international conference on industrial engineering and operations management (pp. 1–6). IIEOM & cosponsored by INFORMS.Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, Article 110059. https://doi.org/10.1016/j. chaos.2020.110059Le Lay, J., Augusto, V., Xie, X., Alfonso-Lizarazo, E., Bongue, B., Celarier, T., … Masmoudi, M. (2020). Impact of covid-19 epidemics on bed requirements in a healthcare center using data-driven discrete-event simulation. In 2020 Winter simulation conference (WSC) (pp. 771–781). IEEE. doi: 10.1109/ WSC48552.2020.9384093.Leung, C. (2020). Risk factors for predicting mortality in elderly patients with COVID-19: A review of clinical data in china. Mechanisms of Ageing and Development, 188. https://doi.org/10.1016/j.mad.2020.111255Li, X., Ge, P., Zhu, J., Li, H., Graham, J., Singer, A., Richman, P. S., & Duong, T. Q. (2020). Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables. PeerJ, 8(December 2019), 1–19. https:// doi.org/10.7717/peerj.10337Lin, G., Feng, X., Guo, W., Cui, X., Liu, S., Jin, W., … Ding, Y. (2021). Electricity theft detection based on stacked autoencoder and the undersampling and resampling based random forest algorithm. IEEE Access, 9, 124044–124058. https://doi.org/ 10.1109/ACCESS.2021.3110510Long, B., Brady, W. J., Bridwell, R. E., Ramzy, M., Montrief, T., Singh, M., & Gottlieb, M. (2021). Electrocardiographic manifestations of COVID-19. American Journal of Emergency Medicine, 41, 96–103. https://doi.org/10.1016/j.ajem.2020.12.060Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F., & Ozceylan, ¨ E. (2022). Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach. International Journal of Fuzzy Systems, 24(2), 1216–1231.Lu, Y., Guan, Y., Zhong, X., Fishe, J. N., & Hogan, T. (2021). Hospital beds planning and admission control policies for COVID-19 pandemic: A hybrid computer simulation approach. In IEEE international conference on automation science and engineering, 2021- Augus (pp. 956–961). doi: 10.1109/CASE49439.2021.9551589.Luo, L., Luo, L., Zhang, X., & He, X. (2017). Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research, 17(1), 1–13.Marwaha, J. S., Beaulieu-Jones, B. R., Kennedy, C. J., Nathanson, L. A., Robinson, K., Tandon, M., & Brat, G. A. (2022). Design, implementation, and clinical impact of a machine learning-assisted intervention bundle to improve opioid prescribing. NEJM Catalyst Innovations in Care Delivery, 3(4), CAT-21.Mauer, E., Lee, J., Choi, J., Zhang, H., Hoffman, K. L., Easthausen, I. J., … Banerjee, S. (2021). A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories. Journal of Biomedical Informatics, 118, Article 103794. https://doi.org/10.1016/j.jbi.2021.103794McGlothlin, J. P., Vedire, S., Srinivasan, H., Madugula, A., Rajagopalan, S., & Khan, L. (2018). Predicting hospital capacity and efficiency. In HEALTHINF 2018 - 11th international conference on health informatics, proceedings; part of 11th international joint conference on biomedical engineering systems and technologies, BIOSTEC 2018, 5 (Biostec) (pp. 562–570). doi: 10.5220/0006658905620570.Mehmood, R., Meriton, R., Graham, G., Hennelly, P., & Kumar, M. (2017). Exploring the influence of big data on city transport operations: A markovian approach. International Journal of Operations and Production Management, 37(1), 75–104. https://doi.org/10.1108/IJOPM-03-2015-0179Mehrabadi, M. A., Aqajari, S. A. H., Azimi, I., Downs, C. A., Dutt, N., & Rahmani, A. M. (2021). Detection of COVID-19 using heart rate and blood pressure: Lessons learned from patients with ARDS. In Paper presented at the proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS (pp. 2140–2143). doi: 10.1109/EMBC46164.2021.9629794.Meijboom, B., Schmidt-Bakx, S., & Westert, G. (2011). Supply chain management practices for improving patient-oriented care. Supply Chain Management, 16(3), 166–175. https://doi.org/10.1108/13598541111127155Mejía, F., Medina, C., Cornejo, E., Morello, E., Vasquez, ´ S., Alave, J., … Malaga, ´ 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(12 December) doi: 10.1371/journal.pone.0244171.Melman, G. J., Parlikad, A. K., & Cameron, E. A. B. (2021). Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation. Health Care Management Science, 19, 356–374. https://doi.org/10.1007/s10729-021- 09548-2Mercatelli, D., Formaggio, F., Caprini, M., Holding, A., & Giorgi, F. M. (2021). Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach. Bioscience Reports, 41(12). https://doi.org/10.1042/ BSR20212218Mesas, A. E., Cavero-Redondo, I., Alvarez-Bueno, ´ C., Cabrera, M. A. S., de Andrade, S. M., Sequí-Dominguez, I., & Martínez-Vizcaíno, V. (2020). Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS ONE, 15(11 November). doi: 10.1371/journal.pone.0241742.Moledina, S. M., Maini, A. A., Gargan, A., Harland, W., Jenney, H., Phillips, G., Thomas, K., Chauhan, D., & Fertleman, M. (2020). Clinical characteristics and predictors of mortality in patients with COVID-19 infection outside intensive care. International Journal of General Medicine, 13, 1157–1165. https://doi.org/10.2147/ IJGM.S271432More, A. S., & Rana, D. P. (2017). Review of random forest classification techniques to resolve data imbalance. In 2017 1st International conference on intelligent systems and information management (ICISIM) (pp. 72–78).Mudatsir, M., Fajar, J. K., Wulandari, L., Soegiarto, G., Ilmawan, M., Purnamasari, Y., … Harapan, H. (2021). Predictors of COVID-19 severity: A systematic review and metaanalysis [version 2; peer review: 2 approved]. F1000Research, 9, 1–26. doi: 10.12688/F1000RESEARCH.26186.2.Muhammad, N., Upadhyay, A., Kumar, A., & Gilani, H. (2022). Achieving operational excellence through the lens of lean and Six Sigma during the COVID-19 pandemic. The International Journal of Logistics Management.Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment. Naymagon, L., Zubizarreta, N., Feld, J., van Gerwen, M., Alsen, M., Thibaud, S., Kessler, A., Venugopal, S., Makki, I., Qin, Q., Dharmapuri, S., Jun, T., Bhalla, S., Berwick, S., Christian, K., Mascarenhas, J., Dembitzer, F., Moshier, E., & Tremblay, D. (2020). Admission D-dimer levels, D-dimer trends, and outcomes in COVID-19. Thrombosis Research, 196(June), 99–105. doi: 10.1016/j.thromres.2020.08.032.Mukhuty, S., Upadhyay, A., & Rothwell, H. (2022). Strategic sustainable development of Industry 4.0 through the lens of social responsibility: The role of human resource practices. Business Strategy and the Environment.Naymagon, L., Zubizarreta, N., Feld, J., van Gerwen, M., Alsen, M., Thibaud, S., Kessler, A., Venugopal, S., Makki, I., Qin, Q., Dharmapuri, S., Jun, T., Bhalla, S., Berwick, S., Christian, K., Mascarenhas, J., Dembitzer, F., Moshier, E., & Tremblay, D. (2020). Admission D-dimer levels, D-dimer trends, and outcomes in COVID-19. Thrombosis Research, 196(June), 99–105. doi: 10.1016/j.thromres.2020.08.032.Nazir, A., & Ampadu, H. K. (2022). Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients. PeerJ Computer Science, 8 (January 2020), e889.NHS (2022). AI Skunkworks projects. Available at: https://transform.england.nhs.uk/ai-la b/ai-lab-programmes/skunkworks/ai-skunkworks-projects/ (Accessed: 12-10-2022). Nijman, S., Leeuwenberg, A., Beekers, I., Verkouter, I., Jacobs, J., Bots, M., Asselbergs, F.,Moons, K., & Debray, T. (2022). Missing data is poorly handled and reported in prediction model studies using machine learning: A literature review. Journal of Clinical Epidemiology, 142, 218–229. https://doi.org/10.1016/j.jclinepi.2021.11.023Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., … Wiegand, T. (2021). Machine learning for health: Algorithm auditing & quality control. Journal of Medical Systems, 45(12). https://doi.org/10.1007/s10916-021- 01783-yOrdu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2021). A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach. Journal of the Operational Research Society, 72(3), 485–500. https://doi.org/10.1080/ 01605682.2019.1700186Ortíz-Barrios, M. A., & Alfaro-Saíz, J. (2020). Methodological approaches to support process improvement in emergency departments: A systematic review. International Journal of Environmental Research and Public Health, 17(8). https://doi.org/10.3390/ ijerph17082664Ortiz-Barrios, M., & Alfaro-Saiz, J. (2020). An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector. PLoS ONE, 15(6 June). doi: 10.1371/journal.pone.0234984.Ortíz-Barrios, M. A., Coba-Blanco, D. M., Alfaro-Saíz, J. J., & Stand-Gonz´ alez, D. (2021). Process improvement approaches for increasing the response of emergency departments against the COVID-19 pandemic: A systematic review. International Journal of Environmental Research and Public Health, 18(16), 8814. https://doi.org/ 10.3390/ijerph18168814Patel, D., Kher, V., Desai, B., Lei, X., Cen, S., Nanda, N., Gholamrezanezhad, A., Duddalwar, V., Varghese, B., & Oberai, A. A. (2021). Machine learning based predictors for COVID-19 disease severity. Scientific Reports, 11(1), 1–7. https://doi. org/10.1038/s41598-021-83967-7Petrilli, C. M., Jones, S. A., Yang, J., Rajagopalan, H., O’Donnell, L., Chernyak, Y., … Horwitz, L. I. (2020). Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: Prospective cohort study. BMJ, 369. https://doi.org/10.1136/bmj.m1966Pezoulas, V. C., Kourou, K. D., Mylona, E., Papaloukas, C., Liontos, A., Biros, D., Milionis, O. I., Kyriakopoulos, C., Kostikas, K., Milionis, H., & Fotiadis, D. I. (2022). ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Computers in Biology and Medicine, 141(November 2021), Article 105176. https://doi.org/10.1016/j. compbiomed.2021.105176Piccialli, F., di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23(6), 1467–1497. https://doi.org/10.1007/s10796-021-10131-x Possik, J., Asgary, A., Solis, A. O., Zacharewicz, G., Shafiee, M. A., Najafabadi, M. M., …Wu, J. (2022). An agent-based modeling and virtual reality application using distributed simulation: Case of a COVID-19 intensive care unit. IEEE Transactions on Engineering Management, 1–13. https://doi.org/10.1109/TEM.2022.3195813Pourhomayoun, M., & Shakibi, M. (2021). Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health, 20, Article 100178. https://doi.org/10.1016/j.smhl.2020.100178Privett, N., & Gonsalvez, D. (2014). The top ten global health supply chain issues: Perspectives from the field. Operations Research for Health Care, 3(4), 226–230.Rahman, N. A. A., Ahmi, A., Jraisat, L., & Upadhyay, A. (2022). Examining the trend of humanitarian supply chain studies: Pre, during and post COVID-19 pandemic. Journal of Humanitarian Logistics and Supply Chain Management (ahead-of-print).Raj, A., Mukherjee, A. A., de Sousa Jabbour, A. B. L., & Srivastava, S. K. (2022). Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. Journal of Business Research, 142, 1125–1139.Rees, E. M., Nightingale, E. S., Jafari, Y., Waterlow, N. R., Clifford, S., Carl, C. A., Group, C. W., Jombart, T., Procter, S. R., & Knight, G. M. (2020). COVID-19 length of hospital stay: A systematic review and data synthesis. BMC Medicine, 18(1). https:// doi.org/10.1186/s12916-020-01726-3Restrepo, M. I., Rousseau, L. M., & Vall´ee, J. (2020). Home healthcare integrated staffing and scheduling. Omega, 95, Article 102057.Robinson, S. (2002). General concepts of quality for discrete-event simulation. European Journal of Operational Research, 138(1), 103–117.Robinson, S. (2005). Discrete-event simulation: From the pioneers to the present, what next? Journal of the Operational Research Society, 56(6), 619–629.Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018). The future and social impact of big data analytics in supply chain management: Results from a Delphi study. Technological Forecasting and Social Change, 130, 135–149. https://doi. org/10.1016/j.techfore.2017.10.005Rostami, M., & Mansouritorghabeh, H. (2020). D-dimer level in COVID-19 infection: A systematic review. Expert Review of Hematology, 13(11), 1265–1275. https://doi.org/ 10.1080/17474086.2020.1831383Sage Growth Report (2021). The state of healthcare automation. Available at: http://go. sage-growth.com/healthcare-automation-success-030921-ms (Accessed: 10-10- 2022).Sala, F., Quarto, M., & D’urso, G. (2022). Simulation study of the impact of COVID-19 policies on the efficiency of a smart clinic MRI service. Healthcare (Switzerland), 10 (4) doi: 10.3390/healthcare10040619.Sawangarreerak, S., & Thanathamathee, P. (2020). Random forest with sampling techniques for handling imbalanced prediction of university student depression. Information, 11(11), 1–13. https://doi.org/10.3390/info11110519Schwab, P., Schütte, A. D. M., Dietz, B., & Bauer, S. (2020). Clinical predictive models for COVID-19: Systematic study. Journal of Medical Internet Research, 22(10). https:// doi.org/10.2196/21439Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1–23.Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022). Using decision tree algorithms for estimating ICU admission of COVID-19 patients. Informatics in Medicine Unlocked, 30(February), Article 100919. https://doi.org/10.1016/j. imu.2022.100919Sharma, M., Kumar, A., Luthra, S., Joshi, S., & Upadhyay, A. (2022). The impact of environmental dynamism on low-carbon practices and digital supply chain networks to enhance sustainable performance: An empirical analysis. Business Strategy and the Environment.Sharma, G., Volgman, A. S., & Michos, E. D. (2020). Sex differences in mortality from COVID-19 pandemic: Are men vulnerable and women protected? JACC: Case Reports, 2(9), 1407–1410. https://doi.org/10.1016/j.jaccas.2020.04.027Sheng, J., Amankwah-Amoah, J., Khan, Z., & Wang, X. (2021). COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164–1183. https://doi.org/ 10.1111/1467-8551.12441Simsekler, M. C. E., Alhashmi, N. H., Azar, E., King, N., Luqman, R. A. M. A., & Al Mulla, A. (2021). Exploring drivers of patient satisfaction using a random forest algorithm. BMC Medical Informatics and Decision Making, 21(1), 1–9. https://doi.org/ 10.1186/s12911-021-01519-5Simsekler, M. C. E., Qazi, A., Alalami, M. A., Ellahham, S., & Ozonoff, A. (2020). Evaluation of patient safety culture using a random forest algorithm. Reliability Engineering and System Safety, 204(April), Article 107186. https://doi.org/10.1016/j. ress.2020.107186Sitepu, S., Mawengkang, H., & Husein, I. (2018). Optimization model for capacity management and bed scheduling for hospital. In IOP conference series: Materials science and engineering (Vol. 300, No. 1, p. 012016). IOP Publishing.Soni, M., Gopalakrishnan, R., Vaishya, R., & Prabu, P. (2020). D-dimer level is a useful predictor for mortality in patients with COVID-19: Analysis of 483 cases. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(6), 2245–2249. https:// doi.org/10.1016/j.dsx.2020.11.007Srivastava, D. K., Kumar, V., Ekren, B. Y., Upadhyay, A., Tyagi, M., & Kumari, A. (2022). Adopting Industry 4.0 by leveraging organisational factors. Technological Forecasting and Social Change, 176, Article 121439.Standfield, L., Comans, T., & Scuffham, P. (2014). Markov modeling and discrete event simulation in health care: A systematic comparison. International Journal of Technology Assessment in Health Care, 30(2), 165–172.St ˇ ˇep´ ankov´ a, O., Aubrecht, P., Kouba, Z., & Mikˇsovský, P. (2003). Preprocessing for data mining and decision support. In Data mining and decision support (pp. 107–117). Boston, MA: Springer. https://doi.org/10.1007/978-1-4615-0286-9_9.Sujatha, R., Venkata Siva Krishna, B., Chatterjee, J. M., Naidu, P. R., Jhanjhi, N. Z., Charita, C., Mariya, E. N., & Baz, M. (2022). Prediction of suitable candidates for covid-19 vaccination. Intelligent Automation and Soft Computing, 32(1), 525–541. doi: 10.32604/iasc.2022.021216.Sumari, S., Ibrahim, R., Zakaria, N. H., & Ab Hamid, A. H. (2013). Comparing three simulation model using taxonomy: System dynamic simulation, discrete event simulation and agent based simulation. International Journal of Management Excellence, 1(3), 54–59.Sun, T. Q. (2021). Adopting artificial intelligence in public healthcare: The effect of social power and learning algorithms. International Journal of Environmental Research and Public Health, 18(23). https://doi.org/10.3390/ijerph182312682Sun, L., Song, F., Shi, N., Liu, F., Li, S., Li, P., … Shi, Y. (2020). Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID19. Journal of Clinical Virology, 128, Article 104431. https://doi.org/10.1016/j. jcv.2020.104431Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: A real-case study. Medical & Biological Engineering & Computing, 1–22. https://doi.org/10.1007/s11517-022- 02525-zThakur, R., Hsu, S. H. Y., & Fontenot, G. (2012). Innovation in healthcare: Issues and future trends. Journal of Business Research, 65(4), 562–569. https://doi.org/ 10.1016/j.jbusres.2011.02.022Tharakan, S., Nomoto, K., Miyashita, S., & Ishikawa, K. (2020). Body temperature correlates with mortality in COVID-19 patients. Critical Care, 24(1). https://doi.org/ 10.1186/s13054-020-03045-8Upadhyay, A., Hernandez, M. J. P., & Balodi, K. C. (2022). Covid-19 Disaster relief projects management: An exploratory study of critical success factors. Operations Management Research, 1–12.Vazquez-Serrano, ´ J. I., Peimbert-García, R. E., & C´ ardenas-Barron, ´ L. E. (2021). Discreteevent simulation modeling in healthcare: A comprehensive review. International Journal of Environmental Research and Public Health, 18(22). https://doi.org/ 10.3390/ijerph182212262Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., … Ferguson, N. M. (2020). Estimates of the severity of coronavirus disease 2019: A model-based analysis. The Lancet Infectious Diseases, 20(6), 669–677. https://doi. org/10.1016/S1473-3099(20)30243-7Vickers, A. J., Cronin, A. M., & Begg, C. B. (2011). One statistical test is sufficient for assessing new predictive markers. BMC Medical Research Methodology, 11(1), 1–7. https://doi.org/10.1186/1471-2288-11-13Vijiyakumar, K., Lavanya, B., Nirmala, I., & Sofia Caroline, S. (2019). Random forest algorithm for the prediction of diabetes. In Paper presented at the 2019 IEEE international conference on system, computation, automation and networking. https:// doi.org/10.1109/ICSCAN.2019.8878802Wang, R. Y., Guo, T. Q., Li, L. G., Jiao, J. Y., & Wang, L. Y. (2020). Predictions of COVID19 infection severity based on co-associations between the SNPs of co-morbid diseases and COVID-19 through machine learning of genetic data. In 2020 IEEE 8th International conference on computer science and network technology (ICCSNT) (pp. 92–96). IEEE. doi: 10.1109/ICCSNT50940.2020.9304990.Wang, Z., Upadhyay, A., & Kumar, A. (2022). A real options approach to growth opportunities and resilience aftermath of the COVID-19 pandemic. Journal of Modelling in Management (ahead-of-print).Wang, K., Gheblawi, M., & Oudit, G. Y. (2020). Angiotensin converting enzyme 2: A double-edged sword. Circulation, 142(5), 426–428. https://doi.org/10.1161/ CIRCULATIONAHA.120.047049Wang, T., Tang, R., Ruan, H., Chen, R., Zhang, Z., Sang, L., & China Medical Treatment Expert Group for COVID-19. (2021). Predictors of fatal outcomes among hospitalized COVID-19 patients with pre-existing hypertension in china. Clinical Respiratory Journal, 15(8), 915–924. https://doi.org/10.1111/crj.13382Wang, Y., Wang, Z., Tse, G., Zhang, L., Wan, E. Y., Guo, Y., Lip, G. Y. H., Li, G., Lu, Z., & Liu, T. (2020). Cardiac arrhythmias in patients with COVID-19. Journal of Arrhythmia, 36(5), 827–836. https://doi.org/10.1002/joa3.12405Whitworth, J. (2020). COVID-19: A fast evolving pandemic. Transactions of the Royal Society of Tropical Medicine and Hygiene, 114(4), 227–228. https://doi.org/10.1093/ trstmh/traa025Wood, R. M., McWilliams, C. J., Thomas, M. J., Bourdeaux, C. P., & Vasilakis, C. (2020). COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care. Health Care Management Science, 23(3), 315–324. https://doi.org/ 10.1007/s10729-020-09511-7Xie, Q., Chen, Y., Hu, Y., Zeng, F., Wang, P., Xu, L., … Zeng, F. (2022). Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Medical Imaging, 22(1). https://doi.org/10.1186/s12880-022-00868-5Yang, P., Yang, G., Qi, J., Sheng, B., Yang, Y., Zhang, S., … Mao, X. (2021). The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: A modelling study. Scientific Reports, 11(1), 1–13. https://doi.org/ 10.1038/s41598-021-82170-yYu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305- zYusriza, F. A., Abdul Rahman, N. A., Jraisat, L., & Upadhyay, A. (2022). Airline catering supply chain performance during pandemic disruption: A bayesian network modelling approach. International Journal of Quality and Reliability Management. https://doi.org/10.1108/IJQRM-01-2022-0027Zhang, X. (2018). Application of discrete event simulation in health care: A systematic review. BMC Health Services Research, 18(1), 1–11. https://doi.org/10.1186/s12913- 018-3456-4Zhang, L., Yan, X., Fan, Q., Liu, H., Liu, X., Liu, Z., & Zhang, Z. (2020). D-dimer levels on admission to predict in-hospital mortality in patients with covid-19. Journal of Thrombosis and Haemostasis, 18(6), 1324–1329. https://doi.org/10.1111/jth.14859Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., Guan, L., Wei, Y., Li, H., Wu, X., Xu, J., Tu, S., Zhang, Y., Chen, H., & Cao, B. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. The Lancet, 395(10229), 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3Zhu, Z., Hen, B. H., & Teow, K. L. (2012). Estimating ICU bed capacity using discrete event simulation. International Journal of Health Care Quality Assurance, 25(2), 134–144. https://doi.org/10.1108/09526861211198290221160Covid-19Discrete-Event Simulation (DES)Artificial Intelligence (AI)Random Forest (RF)Intensive Care Unit (ICU)HealthcarePublicationORIGINALArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic.pdfArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic.pdfArtículosapplication/pdf4489818https://repositorio.cuc.edu.co/bitstreams/acdd4c09-fe86-4f8a-86fa-94593ab2668a/downloadb09aef9d9b9c79f6d9c545b6aaa961bdMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/900c09af-e2bd-42f4-926f-c61bc64870fd/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic.pdf.txtArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic.pdf.txtExtracted texttext/plain164203https://repositorio.cuc.edu.co/bitstreams/315982d6-def1-48a0-8874-6905c0af761b/download8158cc2a921c2444a51cef73c85875bcMD53THUMBNAILArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic.pdf.jpgArtificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic.pdf.jpgGenerated Thumbnailimage/jpeg14422https://repositorio.cuc.edu.co/bitstreams/9eecc2c7-2993-4e14-860a-ab0a96e62dae/download0da299db99ad838a23bccfe48a8496c4MD5411323/10447oai:repositorio.cuc.edu.co:11323/104472024-09-17 11:02:25.188https://creativecommons.org/licenses/by-nc-nd/4.0/© 2023 Elsevier Inc. All rights reserved.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: a case study

Publicaciones similares