Triage of Patients with COVID-19: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction

Neda Sadat; Sharareh R. Niakan Kalhori; Shahrzad Darvishi; Jamileh Kiani; Farhad Abbasi; Batool Amiri; Erfan Javanmardi; Safiyeh Daneshi

doi:10.5812/koomesh-150060

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Journal of Semnan University of Medical Sciences

e-ISSN 2345-3699

p-ISSN 1608-7046

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Abstract
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https://doi.org/10.69107/koomesh-150060

Triage of Patients with COVID-19: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction

Authors

Neda Sadat¹, Sharareh R. Niakan Kalhori^{2, 3,*}, Shahrzad Darvishi⁴, Jamileh Kiani¹, Farhad Abbasi⁵, Batool Amiri

¹, Erfan Javanmardi

¹, Safiyeh Daneshi

¹

1Clinical Research Development Unit, Persian Gulf Martyrs Hospital, Bushehr University of Medical Sciences, Bushehr, Iran

2Department of Health Information Management, Faculty of Paramedicine, Tehran University of Medical Sciences, Tehran, Iran

3PLRI Informatics Research Institute, Medical School Hannover, Technical University of Braunschweig, Braunschweig, Germany

4Imam Khomeini Hospital, Bushehr University of Medical Sciences, Bushehr, Iran

5Department of Infectious Diseases, Faculty of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran

*Corresponding Author: Department of Health Information Management, Faculty of Paramedicine, Tehran University of Medical Sciences, Tehran, Iran. Email: [email protected]

Koomesh:Vol. 26, issue 1; e150060

Published online:Oct 12, 2024

Article type:Research Article

How to Cite:Sadat N, R. Niakan Kalhori S, Darvishi S, Kiani J, Abbasi F, et al. Triage of Patients with COVID-19: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction. koomesh. 2024;26(1):e150060. doi: https://doi.org/10.69107/koomesh-150060

Abstract

Background: Early identification of high-risk patients with COVID-19 using non-laboratory data at the time of admission may help the effective use of limited healthcare resources and improve clinical decision-making which reduces cost and time, and consequently the death of patients.

Objectives: This study aims to provide an intelligent approach to triaging COVID-19 patients by analyzing effective risk factors and predicting the risk of death due to COVID-19 using an ensemble learning method.

Methods: This is a descriptive-applied study that was conducted in 2021. Non-laboratory data were used during the admission of 4558 confirmed patients with COVID-19 referred to the Shohaday-e-Khalij-e-Fars Hospital in Bushehr registered in the medical care information system. After data preprocessing, the risk factors affecting the death were identified and ranked in importance. The ensemble learning (voting) method was used to develop the death prediction model, and the confusion matrix criteria evaluated its performance.

Results: From a total of 5433 patient records, the data of 4558 cases were included in the study, of which 2222 (45.5%) were women and 2663 (64.5%) were men. The average age of the patients was 47.6. Out of all the investigated factors, 17 characteristics were identified as effective in predicting the death of patients, and their ranking indicated that the first six factors for predicting the death of patients are state of blood oxygen, age, state of consciousness, fever, cough, and body pain, respectively. The proposed model estimated the risk of death with an accuracy of 85%, sensitivity of 85%, specificity of 0.85%, and AUROC of 0.91.

Conclusions: The results of the study provide a low-cost, fast, and innovative solution for the early identification of patients with COVID-19 at risk of death to triage them more effectively, which can be used by the managers of healthcare systems to manage resources by continuously updating new data and providing appropriate treatments for high-risk patients.

Highlights

KeywordsCOVID-19 Machine Learning Risk Factors Coronavirus Infection triage

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References

1.
Organization WH. Coronavirus disease 2019 (‎ COVID-19)‎: situation report, 94. 2020.
2.
Wu C, Chen X, Cai Y, Zhou X, Xu S, Huang H, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934-43. https://doi.org/https://doi.org/10.1001/jamainternmed.2020.0994.
3.
Hui DS, Azhar EI, Kim Y-J, Memish ZA, Oh M-d, Zumla A. Middle East respiratory syndrome coronavirus: risk factors and determinants of primary, household, and nosocomial transmission. Lancet Infect Dis. 2018;18(8):e217-e27.
4.
Pardhan S, Vaughan M, Zhang J, Smith L, Chichger H. Sore eyes as the most significant ocular symptom experienced by people with COVID-19: a comparison between pre-COVID-19 and during COVID-19 states. BMJ Open Ophthalmol. 2020;5(1):e000632.
5.
Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology. 2020;296(2):E86-E96. https://doi.org/https://doi.org/10.1148/radiol.2020201433.
6.
Hui DS, Azhar EI, Madani TA, Ntoumi F, Kock R, Dar O, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis. 2020;91:264-6.
7.
Oran DP, Topol EJ. The proportion of SARS-CoV-2 infections that are asymptomatic: a systematic review. Ann Intern Med. 2021;174(5):655-62.
8.
Lazzerini M, Putoto G. COVID-19 in Italy: momentous decisions and many uncertainties. Lancet Glob Health. 2020;8(5):e641-e2.
9.
Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response. Jama. 2020;323(16):1545-6. https://doi.org/https://doi.org/10.1001/jama.2020.4031.
10.
Moghadas SM, Shoukat A, Fitzpatrick MC, Wells CR, Sah P, Pandey A, et al. Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proc Natl Acad Sci U S A. 2020;117(16):9122-6. https://doi.org/ https://doi.org/10.1073/pnas.2004064117.
11.
Lai C-C, Shih T-P, Ko W-C, Tang H-J, Hsueh P-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int J Antimicrob Agents. 2020;55(3):105924.
12.
Hussain A, Bhowmik B, do Vale Moreira NC. COVID-19 and diabetes: Knowledge in progress. Diabetes Res Clin Pract. 2020;162:108142.
13.
Kabir MJ, Heidari A, Moeini S, Khatirnamani Z, Kavian Telouri F, Eimery M. Calculation of Direct Medical Costs and Indirect Costs in Patients with Covid-19 Hospitalized in the Intensive Care Unit in Golestan Province. Manage Strat Health Syst. 2022;6(4):308-16.
14.
Gao Y, Cai G-Y, Fang W, Li H-Y, Wang S-Y, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020;11(1):1-10.
15.
Organization WH. Maintaining essential health services: operational guidance for the COVID-19 context: interim guidance, 1 June 2020: World Health Organization; 2020 Contract No.: Document Number|.
16.
Zhao Z, Chen A, Hou W, Graham JM, Li H, Richman PS, et al. Prediction model and risk scores of ICU admission and mortality in COVID-19. PloS one. 2020;15(7):e0236618.
17.
Hu H, Yao N, Qiu Y. Comparing rapid scoring systems in mortality prediction of critically ill patients with novel coronavirus disease. Acad Emerg Med. 2020;27(6):461-8.
18.
Shanbehzadeh M, Orooji A, Kazemi-Arpanahi H. Comparing of data mining techniques for predicting in-hospital mortality among patients with covid-19. J Biostat Epidemiol. 2021;7(2):154-73.
19.
Josephus BO, Nawir AH, Wijaya E, Moniaga JV, Ohyver M. Predict mortality in patients infected with COVID-19 virus based on observed characteristics of the patient using logistic regression. Procedia Comput Sci. 2021;179:871-7.
20.
Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, et al. China medical treatment expert group for Covid-19. N Engl J Med. 2019;382(18):1708-20.
21.
Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. Jama. 2020;323(20):2052-9.
22.
Yan L, Zhang H-T, Goncalves J, Xiao Y, Wang M, Guo Y, et al. An interpretable mortality prediction model for COVID-19 patients. Nat Mach Intell. 2020;2(5):283-8.
23.
Malki Z, Atlam E-S, Hassanien AE, Dagnew G, Elhosseini MA, Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos Solitons Fractals. 2020;138:110137.
24.
Cho A. AI systems aim to sniff out coronavirus outbreaks. American Association for the Advancement of Science; 2020.
25.
Xiong Z, Wang R, Bai H, Halsey K, Mei J, Li Y, et al. Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology. 2020;296(3):E156-E65. https://doi.org/https://doi.org/10.1148/radiol.2020201491.
26.
Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J. 2020;56(2). https://doi.org/https://doi.org/10.1183/13993003.01104-2020.
27.
Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. bmj. 2020;369. https://doi.org/https://doi.org/10.1136/bmj.m1328.
28.
Chowdhury ME, Rahman T, Khandakar A, Al-Madeed S, Zughaier SM, Doi SA, et al. An early warning tool for predicting mortality risk of COVID-19 patients using machine learning. Cognit Comput. 2021:1-16. https://doi.org/https://doi.org/10.1007/s12559-020-09812-7.
29.
Nemati M, Ansary J, Nemati N. Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns. 2020;1(5):100074. https://doi.org/https://doi.org/10.1016/j.patter.2020.100074.
30.
Ikemura K, Bellin E, Yagi Y, Billett H, Saada M, Simone K, et al. Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study. J Med Internet Res. 2021;23(2):e23458. https://doi.org/https://doi.org/10.2196/23458.
31.
Patel D, Kher V, Desai B, Lei X, Cen S, Nanda N, et al. Machine learning based predictors for COVID-19 disease severity. Sci Rep. 2021;11(1):1-7. https://doi.org/https://doi.org/10.1038/s41598-021-83967-7.
32.
Vaid A, Somani S, Russak AJ, De Freitas JK, Chaudhry FF, Paranjpe I, et al. Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: Model development and validation. J Med Internet Res. 2020;22(11):e24018. https://doi.org/https://doi.org/10.2196/24018.
33.
Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Med Inform Decis Mak. 2022;22(1):1-12.
34.
Yan L, Zhang H-T, Goncalves J, Xiao Y, Wang M, Guo Y, et al. A machine learning-based model for survival prediction in patients with severe COVID-19 infection. MedRxiv. 2020.
35.
Xie J, Hungerford D, Chen H, Abrams ST, Li S, Wang G, et al. Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19. MedRxiv. 2020.
36.
Shanbezadeh M, Valinejadi A, Afrah R, Kazemi-Arpanahi H, Orooji A, Kaffashian M. Comparison of machine-learning algorithms efficiency to build a predictive model for mortality risk in COVID-19 hospitalized patients. Faslnamahi Kumish. 2022;24(1):128-38.
37.
Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180(8):1081-9. https://doi.org/https://doi.org/10.1001/jamainternmed.2020.2033.
38.
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. lancet. 2020;395(10229):1054-62. https://doi.org/https://doi.org/10.1016/S0140-6736(20)30566-3.
39.
Toussie D, Voutsinas N, Finkelstein M, Cedillo MA, Manna S, Maron SZ, et al. Clinical and chest radiography features determine patient outcomes in young and middle-aged adults with COVID-19. Radiology. 2020;297(1):E197-E206. https://doi.org/https://doi.org/10.1148/radiol.2020201754.
40.
Karthikeyan A, Garg A, Vinod P, Priyakumar UD. Machine learning based clinical decision support system for early COVID-19 mortality prediction. Front Public Health. 2021;9:626697.
41.
An C, Lim H, Kim D-W, Chang JH, Choi YJ, Kim SW. Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study. Sci Rep. 2020;10(1):1-11.
42.
Kuncheva LI. Combining pattern classifiers: methods and algorithms. John Wiley & Sons; 2014.
43.
Jafari M, Akbari M, Navidkia M, Dashtbin S, Mousavi SF, Heidary M, et al. Comparison of clinical, radiological and laboratory findings in discharged and dead patients with COVID-19. Vacunas. 2022.
44.
Talebi S, Nematshahi M, Tajabadi A, Khosrogerdi A. Comparison of clinical and epidemiological characteristics of deceased and recovered patients with COVID-19 in Sabzevar, Iran. Journal Mil Med. 2020;22(6):509-16.
45.
Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis. 2020;34:101623.
46.
Cao Y, Liu X, Xiong L, Cai K. Imaging and clinical features of patients with 2019 novel coronavirus SARS‐CoV‐2: a systematic review and meta‐analysis. J Med Virol. 2020;92(9):1449-59.
47.
Hassan SA, Sheikh FN, Jamal S, Ezeh JK, Akhtar A. Coronavirus (COVID-19): a review of clinical features, diagnosis, and treatment. Cureus. 2020;12(3).
48.
Alipio M, Pregoner JD. Epidemiological Characteristics of An Outbreak of Coronavirus Disease 2019 in the Philippines (April 3, 2020). Available at SSRN 3568934.
49.
Hooper GL. Health Seeking in Men: A Concept Analysis. Urol Nurs. 2016;36(4).
50.
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. lancet. 2020;395(10223):497-506.
51.
Ki M. Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea. Epidemiol Health. 2020;42.
52.
Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. bmj. 2020;368.
53.
Liu Y, Mao B, Liang S, Yang J. Association between age and clinical characteristics and outcomes of COVID‐19. Eur Respir J;55(5):2001112. https://doi.org/https://doi.org/10.1183/13993003.01112-2020.
54.
Mehra MR, Desai SS, Kuy S, Henry TD, Patel AN. Retraction: cardiovascular disease, drug therapy, and mortality in Covid-19. N Engl J Med. DOI: 10.1056/NEJMoa2007621. Mass Medical Soc; 2020.
55.
Bajgain KT, Badal S, Bajgain BB, Santana MJ. Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature. Am J Infect Control. 2021;49(2):238-46. https://doi.org/https://doi.org/10.1016/j.ajic.2020.06.213.
56.
Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, et al. A tool for early prediction of severe coronavirus disease 2019 (COVID-19): a multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin Infect Dis. 2020;71(15):833-40.
57.
Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, et al. A tool to early predict severe 2019-novel coronavirus pneumonia (COVID-19): a multicenter study using the risk nomogram in Wuhan and Guangdong, China. MedRxiv. 2020. https://doi.org/ https://doi.org/10.1101/2020.03.17.20037515.
58.
Estiri H, Strasser ZH, Klann JG, Naseri P, Wagholikar KB, Murphy SN. Predicting COVID-19 mortality with electronic medical records. NPJ Digit Med. 2021;4(1):1-10. https://doi.org/https://doi.org/10.1038/s41746-021-00383-x.
59.
Podder P, Mondal MRH, editors. Machine Learning to Predict COVID-19 and ICU Requirement. 2020 11th International Conference on Electrical and Computer Engineering (ICECE); 2020. IEEE.

Copyright

© 2024, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.

Outline

Triage of Patients with COVID-19: Using Ensemble Learning Method for Risk Factor Analysis and Death Prediction

Abstract

Highlights

Fulltext

References

Copyright

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