Document Type : Original Article
Authors
1 Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
2 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
3 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
Abstract
BACKGROUND: Accurately predicting the intubation risk in COVID‑19 patients at the admission
time is critical to optimal use of limited hospital resources, providing customized and evidence‑based
treatments, and improving the quality of delivered medical care services. This study aimed to design
a statistical algorithm to select the best features influencing intubation prediction in coronavirus
disease 2019 (COVID‑19) hospitalized patients. Then, using selected features, multiple artificial
neural network (ANN) configurations were developed to predict intubation risk.
MATERIAL AND METHODS: In this retrospective single‑center study, a dataset containing 482
COVID‑19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First,
the Phi correlation coefficient method was performed for selecting the most important features affecting
COVID‑19 patients’ intubation. Then, the different configurations of ANN were developed. Finally,
the performance of ANN configurations was assessed using several evaluation metrics, and the best
structure was determined for predicting intubation requirements among hospitalized COVID‑19 patients.
RESULTS: The ANN models were developed based on 18 validated features. The results indicated
that the best performance belongs to the 18‑20‑1 ANN configuration with positive predictive value
(PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and
area under curve (AUC) = 0.906.
CONCLUSIONS: The results demonstrate the effectiveness of the ANN models for timely and reliable
prediction of intubation risk in COVID‑19 hospitalized patients. Our models can inform clinicians and
those involved in policymaking and decision making for prioritizing restricted mechanical ventilation
and other related resources for critically COVID‑19 patients.
Keywords
Bukachi S. Lockdowns, lives and livelihoods: The impact of
COVID‑19 and public health responses to conflict affected
populations‑a remote qualitative study in Baidoa and Mogadishu,
Somalia. Confl Health 2021;15:47. doi: 10.1186/s13031‑021‑00382‑5.
2. Saji JA, Babu BP, Sebastian SR. Social influence of COVID‑19:
An observational study on the social impact of post‑COVID‑19
lockdown on everyday life in Kerala from a community
perspective. J Educ Health Promot 2020;9:360. doi: 10.4103/jehp.
jehp_650_20.
3. Yazdani A, Sharifian R, Ravangard R, Zahmatkeshan M.
COVID‑19 and information communication technology:
A conceptual model. J Adv Pharm Educ Res 2021;11(S1):83‑97.
4. 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;395:1054‑62.
5. Chow N, Fleming‑Dutra K, Gierke R, Hall A, Hughes M,
Pilishvili T, et al. CDC COVID‑19 Response Team. Preliminary
estimates of the prevalence of selected underlying health
conditions among patients with coronavirus disease 2019—United
States, February 12–March 28, 2020. MMWR Morb Mortal Wkly
Rep 2020;69:382‑6.
6. Leclerc T, Donat N, Donat A, Pasquier P, Libert N, Schaeffer E,
et al. Prioritisation of ICU treatments for critically ill patients in
a COVID‑19 pandemic with scarce resources. Anaesth Crit Care
Pain Med 2020;39:333‑9.
7. Wang H, Zhou X, Jia X, Song C, Luo X, Zhang H, et al. Emotional
exhaustion in frontline healthcare workers during the COVID‑19
pandemic in Wuhan, China: The effects of time pressure, social
sharing and cognitive appraisal. BMC Public Health 2021;21. 829.
doi: 10.1186/s12889‑021‑10891‑w.
8. Gana W, Nkodo JA, Fougère B. Medical decision making during
the COVID‑19 epidemic: An opportunity to think how we think.
Diagnosis 2021;8:400‑1.
9. 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.
doi: 10.3389/fpubh. 2021.626697.
10. Shanbezadeh M, Soltani T, Ahmadi M. Developing a clinical
decision support model to evaluate the quality of asthma control
level. Middle‑East J Sci Res 2013;14:387‑93.
11. Li X, Liao H, Wen Z. A consensus model to manage the
non‑cooperative behaviors of individuals in uncertain group
decision making problems during the COVID‑19 outbreak. Appl
Soft Comput 2021;99:106879. doi: 10.1016/j.asoc. 2020.106879.
12. Hawkins A, Stapleton S, Rodriguez G, Gonzalez RM, Baker WE.
Emergency tracheal intubation in patients with COVID‑19:
A single‑center, retrospective cohort study. West J Emerg Med
2021;22:678‑86.
13. Zhang K, Jiang X, Madadi M, Chen L, Savitz S, Shams S, editors.
DBNet: A novel deep learning framework for mechanical
ventilation prediction using electronic health records. Proceedings
of the 12th ACM Conference on Bioinformatics, Computational
Biology, and Health Informatics, 2021.
14. Khan M, Mehran MT, Haq ZU, Ullah Z, Naqvi SR, Ihsan M, et al.
Applications of artificial intelligence in COVID‑19 pandemic:
A comprehensive review. Expert Systems with Applications.
2021;185:115695. doi: 10.1016/j.eswa. 2021.115695.
15. Rodríguez‑Rodríguez I, Rodríguez JV, Shirvanizadeh N, Ortiz A,
Pardo‑Quiles DJ. Applications of artificial intelligence, machine
learning, big data and the internet of things to the COVID‑19
pandemic: A scientometric review using text mining. Int J Environ
Res Public Health 2021;18:8578. doi: 10.3390/ijerph 18168578.
16. LokeshkumarR, Mishra OA, Kalra S. Social media data analysis to
predict mental state of users using machine learning techniques.
J Educ Health Promot 2021;10:301. doi: 10.4103/jehp.jehp_446_20.
17. Shu S, Ren J, Song J. Clinical application of machine learning‑based
artificial intelligence in the diagnosis, prediction, and classification
of cardiovascular diseases. Circ J 2021;85:1416‑25.
18. Alballa N, Al‑Turaiki I. Machine learning approaches in
COVID‑19 diagnosis, mortality, and severity risk prediction:
A review. Inform Med Unlocked 2021;24:100564. doi: 10.1016/j.
imu. 2021.100564.
19. Assaf D, Gutman Ya, Neuman Y, Segal G, Amit S, Gefen‑Halevi S,
et al. Utilization of machine‑learning models to accurately predict
the risk for critical COVID‑19. Intern Emerg Med 2020;15:1435‑43.
20. Agieb R. Machine learning models for the prediction the necessity
of resorting to icu of covid‑19 patients. Int J Adv Trends Comput
Sci Eng 2020:9:6980‑4.
21. Ryan L, Lam C, Mataraso S, Allen A, Green‑Saxena A, Pellegrini E,
et al. Mortality prediction model for the triage of COVID‑19,
pneumonia, and mechanically ventilated ICU patients:
A retrospective study. Ann Med Surg 2020;59:207‑16.
22. 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:e0236618. doi: 10.1371/journal.
pone. 0236618.
23. Allenbach Y, Saadoun D, Maalouf G, Vieira M, Hellio A,
Boddaert J, et al. Development of a multivariate prediction model of intensive care unit transfer or death: A French prospective
cohort study of hospitalized COVID‑19 patients. PloS One
2020;15:e0240711. doi: 10.1371/journal.pone. 0240711.
24. Pan P, Li Y, Xiao Y, Han B, Su L, Su M, et al. Prognostic assessment
of COVID‑19 in the intensive care unit by machine learning
methods: Model development and validation. J Med Internet Res
2020;;22:e23128. doi: 10.2196/23128.
25. Arvind V, Kim JS, Cho BH, Geng E, Cho SK. Development
of a machine learning algorithm to predict intubation among
hospitalized patients with COVID‑19. J Crit Care 2021;62:25‑30.
26. 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;11:1‑10.
27. Hernandez‑Suarez DF, Ranka S, Kim Y, Latib A, Wiley J,
Lopez‑Candales A, et al. Machine‑learning‑based in‑hospital
mortality prediction for transcatheter mitral valve repair in the
United States. Cardiovasc Revasc Med 2021;22:22‑8.
28. Parchure P, Joshi H, Dharmarajan K, Freeman R, Reich DL,
Mazumdar M, et al. Development and validation of a machine
learning‑based prediction model for near‑term in‑hospital
mortality among patients with COVID‑19. BMJ Supportive Palliat
Care 2020. doi: 10.1136/bmjspcare‑2020‑002602.
29. Vaid A, Jaladanki SK, Xu J, Teng S, Kumar A, Lee S, et al.
Federated learning of electronic health records to improve
mortality prediction in hospitalized patients with COVID‑19:
Machine learning approach. JMIR Med Inform 2021;9:e24207.
doi: 10.2196/24207.
30. Yadaw AS, Li Y‑C, Bose S, Iyengar R, Bunyavanich S, Pandey G.
Clinical features of COVID‑19 mortality: Development and
validation of a clinical prediction model. Lancet Digit Health
2020;2:e516‑25.
31. 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;2:283‑8.
32. Phyu TZ, Oo NN, editors. Performance comparison of feature
selection methods. MATEC web of conferences; France. EDP
Sciences; 2016.
33. Venkatesh B, Anuradha J. A review of feature selection and its
methods. Cybern Inf Technol 2019;19:3‑26.
34. Shanmuganathan S. Artificial neural network modelling: An
introduction. Artificial Neural Network Modelling. Switzerland:
Springer; 2016. p. 1‑14.
35. Al‑Massri R, Al‑Astel Y, Ziadia H, Mousa DK, Abu‑Naser SS.
Classification prediction of SBRCTs cancers using artificial neural
network. Int J Acad Eng Res 2018;2.
36. Da Silva IN, Spatti DH, Flauzino RA, Liboni LHB, dos Reis
Alves SF. Artificial neural network architectures and training
processes. Artificial Neural Networks. New York, USA: Springer;
2017. p. 21‑8.
37. Walczak S. Artificial neural networks. Encyclopedia of
Information Science and Technology. 4th ed. South Florida, USA:
IGI Global; 2018. p. 120‑31.
38. Ibrahim M N, Mohammed O Al‑Shawwa, Samy S Abu‑Naser.
A proposed artificial neural network for predicting movies rates
category. Int J Acad Eng Res 2019;3:21‑5.
39. Shahmoradi L, Liraki Z, Karami M, Savareh BA, Nosratabadi M.
Development of decision support system to predict neurofeedback
response in ADHD: An artificial neural network approach. Acta
Inform Med 2019;27:186‑91.
40. Sapna S, Tamilarasi A, Kumar MP. Backpropagation learning
algorithm based on Levenberg Marquardt algorithm. Comp Sci
Inform Technol 2012;2:393‑8.
41. Dorofki M, Elshafie AH, Jaafar O, Karim OA, Mastura S.
Comparison of artificial neural network transfer functions abilities
to simulate extreme runoff data. Int Proc Chem Biol Environ Eng
2012;33:39‑44.
42. de Fátima Cobre A, Stremel DP, Noleto GR, Fachi MM, Surek M,
Wiens A, et al. Diagnosis and prediction of COVID‑19 severity:
Can biochemical tests and machine learning be used as prognostic
indicators? Comput Biol Med 2021;134:104531. doi: 10.1016/j.
compbiomed. 2021.104531.
43. Domínguez-Olmedo JL, Gragera-Martínez Á, Mata J, Álvarez VP.
Machine learning applied to clinical laboratory data in Spain
for COVID-19 outcome prediction: model development and
validation. Journal of medical Internet research 2021;23(4):e26211.
44. Burdick H, Lam C, Mataraso S, Siefkas A, Braden G,
Dellinger RP, et al. Prediction of respiratory decompensation
in Covid‑19 patients using machine learning: The READY trial.
Comput Biol Med 2020;124:103949. doi: 10.1016/j.compbiomed.
2020.103949.
45. Foieni F, Sala G, Mognarelli JG, Suigo G, Zampini D, Pistoia M,
et al. Derivation and validation of the clinical prediction model
for COVID‑19. Intern Emerg Med 2020;15:1409‑14.
)