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ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREDICTION OF COVID-19 MORTALITY AND ITS RELATED FACTORS: A MULTI-CENTER STUDY
Keywords:
artificial intelligence, machine learning, random forest, COVID-19, mortality.DOI:
https://doi.org/10.17654/0972361723064Abstract
Predictive artificial intelligence (AI) models for the assessment of factors and trends related to disease prevalence, prognosis, and the risk prediction of mortality in the early phases, seem to be effective techniques. Machine learning (ML) algorithms as a branch of AI methods have had a considerable role in controlling and management of COVID-19 and previous pandemics. The present study estimates the mortality risk among COVID-19 patients based on ML algorithms, prediction of outcomes in new cases, and also determines the top significant features related to morality in the patients. In this retrospective research, 7115 patients with confirmed COVID-19 disease were included in the study. The information was collected from 35 different hospitals across Khorasan-Razavi province, Northeast of Iran during 20 February 2020 - 21 June 2021. We employed random forests (RF), decision tree (DT), artificial neural networks (ANN), support vector machines (SVM), logistic regression (LR), and boosted trees (BT) ML algorithms. Our findings indicated that the RF model had better performance in different criteria. The RF algorithm had area under curve (AUC) of 88%, accuracy of 96%, precision of 96%, sensitivity of 99%, and specificity of 80%. Intubation, SpO2, age, time from symptoms, loss of consciousness, reception season, and diabetes were the seven ordered and significant predictors of the risk of COVID-19 patients’ death. RF model can aid in clinical decision-making and limited resource allocation. This model needs to be externally validated in larger populations, more features, and multicenter settings.
Received: August 4, 2023
Accepted: September 13, 2023
References
G. Sousa et al., Mortality and survival of COVID-19, Epidemiology and Infection, 2020.
S. Torabi et al., Androgenetic alopecia in women and men is not related to COVID-19 infection severity: a prospective cohort study of hospitalized COVID-19 patients, Journal of the European Academy of Dermatology and Venereology 35(9) (2021), e553-e556.
M. A. Shereen et al., COVID-19 infection: emergence, transmission, and characteristics of human coronaviruses, Journal of Advanced Research 24 (2020), 91-98.
R. Verity, et al., Estimates of the severity of COVID-19 disease, MedRxiv, 2020: p. 2020.03. 09.20033357.
Organization, W. H. WHO Coronavirus (COVID-19) Dashboard. 2023 22, July 2023. Available from: https://covid19.who.int./.
Iranian Ministry of Health and Medical Education (MOHME), 2023. Available from: https://ird.behdasht.gov.ir./.
Y. Gao et al., Machine learning based early warning system enables accurate mortality risk prediction for COVID-19, Nature Communications 11(1) (2020), 5033.
Z. Allam and D. S. Jones, On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management, Healthcare 8 (2020), 46.
S. N. Chaudhari et al., Role of Internet of Things (IOT) in pandemic Covid-19 condition, International Journal of Engineering Research and Applications 9 (2020), 57-61.
T. Kuno et al., Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir, Journal of Medical Virology 94(3) (2022), 958-964.
F. Cabitza et al., Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests, Clinical Chemistry and Laboratory Medicine (CCLM) 59(2) (2021), 421-431.
V. Chamola et al., A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact, IEEE Access 8 (2020), 90225-90265.
M. Montazeri, Machine learning models for predicting the diagnosis of liver disease, Koomesh 16(1) (2014), 53-59 (in Persian).
A. S. Albahri et al., Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review, Journal of Medical Systems 44 (2020), 1-11.
E. Surkova, V. Nikolayevskyy and F. Drobniewski, False-positive COVID-19 results: hidden problems and costs, The Lancet Respiratory Medicine 8(12) (2020), 1167-1168.
N. Shaverdian et al., Need for caution in the diagnosis of radiation pneumonitis during the covid-19 pandemic, Advances in Radiation Oncology 5(4) (2020), 617-620.
C. P. West, V. M. Montori and P. Sampathkumar, COVID-19 testing: the threat of false-negative results, Mayo Clinic Proceedings, Elsevier, 2020.
V. Bayat et al., A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prediction model from standard laboratory tests, Clinical Infectious Diseases 73(9) (2021), e2901-e2907.
F. Barboza, H. Kimura and E. Altman, Machine learning models and bankruptcy prediction, Expert Systems with Applications 83 (2017), 405-417.
M. Saqlain, B. Jargalsaikhan and J. Y. Lee, A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing, IEEE Transactions on Semiconductor Manufacturing 32(2) (2019), 171-182.
L. Zhu et al., Big data analytics in intelligent transportation systems: a survey, IEEE Transactions on Intelligent Transportation Systems 20(1) (2018), 383-398.
H. Samin and T. Azim, Knowledge based recommender system for academia using machine learning: a case study on higher education landscape of Pakistan, IEEE Access 7 (2019), 67081-67093.
A. L. Booth, E. Abels and P. McCaffrey, Development of a prognostic model for mortality in COVID-19 infection using machine learning, Modern Pathology 34(3) (2021), 522-531.
S. Lalmuanawma, J. Hussain and L. Chhakchhuak, Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review, Chaos Solitons Fractals 139 (2020), 110059.
M. M. Banoei et al., Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying, Critical Care 25(1) (2021), 1-14.
G. F. Silva et al., Machine learning for hypertension prediction: a systematic review, Current Hypertension Reports 24(11) (2022), 523-533.
Y. W. Lee, J. W. Choi and E.-H. Shin, Machine learning model for predicting malaria using clinical information, Computers in Biology and Medicine 129 (2021), 104151.
J.-M. Kwon et al., Artificial intelligence algorithm for predicting mortality of patients with acute heart failure, PLoS ONE 14(7) (2019), e0219302.
H. Fu et al., External validation of a prognostic model for 1-year survival after fragility hip fracture: a retrospective cohort study, 2022 (preprint).
H. Motulsky, Prism 5 statistics guide, GraphPad Software 31(1) (2007), 39-42.
K. Moulaei et al., Predicting mortality of COVID-19 patients based on data mining techniques, Journal of Biomedical Physics and Engineering 11(5) (2021), 653.
K. Moulaei et al., Comparing machine learning algorithms for predicting COVID-19 mortality, BMC Medical Informatics and Decision Making 22(1) (2022), 1-12.
E. Jamshidi et al., Using machine learning to predict mortality for COVID-19 patients on day 0 in the ICU, Frontiers in Digital Health 3 (2022), 681608.
P. Parchure 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 & Palliative Care 12(e3) (2022), e424-e431.
S. Subudhi et al., Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19, NPJ Digital Medicine 4(1) (2021), 87.
V. K. Gupta et al., Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model, Big Data Mining and Analytics 4(2) (2021), 116-123.
T. P. F. Lima et al., Death risk and the importance of clinical features in elderly people with COVID-19 using the Random Forest Algorithm, Revista Brasileira de Saude Materno Infantil 21 (2021), 445-451.
E. Cornelius, O. Akman and D. Hrozencik, COVID-19 mortality prediction using machine learning-integrated random forest algorithm under varying patient frailty, Mathematics 9(17) (2021), 2043.
H. Khadem et al., COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: machine learning models integrated with interpretation framework, Computers in Biology and Medicine 144 (2022), 105361.
J. Wang et al., A descriptive study of random forest algorithm for predicting COVID-19 patients outcome, Peer J. 8 (2020), 1-19.
G. Salinas-Escudero et al., A survival analysis of COVID-19 in the Mexican population, BMC Public Health 20(1) (2020), 1-8.
S. Li et al., Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method, Neural Computing and Applications 35 (2021), 1-10.
T. J. Levy et al., Development and validation of a survival calculator for hospitalized patients with COVID-19, MedRxiv, 2020.
D. Bertsimas et al., COVID-19 mortality risk assessment: an international multi-center study, PLoS ONE 15(12) (2020), e0243262.
K. Wang et al., Clinical and laboratory predictors of in-hospital mortality in patients with coronavirus disease-2019: a cohort study in Wuhan, China, Clinical Infectious Diseases 71(16) (2020), 2079-2088.
Z. Zhao et al., Prediction model and risk scores of ICU admission and mortality in COVID-19, PLoS ONE 15(7) (2020), e0236618.
J. Xie et al., Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19, MedRxiv, 2020.
P. Du et al., A systematic review and meta-analysis of risk factors associated with severity and death in COVID-19 patients, Canadian Journal of Infectious Diseases and Medical Microbiology (2021), 1-12.
M. Mahdavi et al., A machine learning based exploration of COVID-19 mortality risk, PLoS ONE 16(7) (2021), e0252384.
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