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Article

Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients

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Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK
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Leeds Sustainability Institute, Leeds Beckett University, Leeds LS1 3HE, UK
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Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 5FB, UK
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Centre for Environment and Agricultural Informatics, Cranfield University, Bedfordshire MK43 0AL, UK
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Department of Biology, Faculty of Science, University Putra Malaysia, Serdang, Selangor 43400, Malaysia
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Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran
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Faculty of Mathematical Sciences & Statistics, Malayer University, Malayer 6571995863, Iran
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Authors to whom correspondence should be addressed.
Academic Editors: Alireza Daneshkhah, Amin Hosseinian-Far and Samer A. Kharroubi
Int. J. Environ. Res. Public Health 2021, 18(12), 6228; https://doi.org/10.3390/ijerph18126228
Received: 18 April 2021 / Revised: 28 May 2021 / Accepted: 1 June 2021 / Published: 9 June 2021
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools. View Full-Text
Keywords: Bayesian network; COVID-19; SARS CoV; random forest; risk stratification; synthetic minority oversampling technique (SMOTE) Bayesian network; COVID-19; SARS CoV; random forest; risk stratification; synthetic minority oversampling technique (SMOTE)
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MDPI and ACS Style

Vepa, A.; Saleem, A.; Rakhshan, K.; Daneshkhah, A.; Sedighi, T.; Shohaimi, S.; Omar, A.; Salari, N.; Chatrabgoun, O.; Dharmaraj, D.; Sami, J.; Parekh, S.; Ibrahim, M.; Raza, M.; Kapila, P.; Chakrabarti, P. Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients. Int. J. Environ. Res. Public Health 2021, 18, 6228. https://doi.org/10.3390/ijerph18126228

AMA Style

Vepa A, Saleem A, Rakhshan K, Daneshkhah A, Sedighi T, Shohaimi S, Omar A, Salari N, Chatrabgoun O, Dharmaraj D, Sami J, Parekh S, Ibrahim M, Raza M, Kapila P, Chakrabarti P. Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients. International Journal of Environmental Research and Public Health. 2021; 18(12):6228. https://doi.org/10.3390/ijerph18126228

Chicago/Turabian Style

Vepa, Abhinav, Amer Saleem, Kambiz Rakhshan, Alireza Daneshkhah, Tabassom Sedighi, Shamarina Shohaimi, Amr Omar, Nader Salari, Omid Chatrabgoun, Diana Dharmaraj, Junaid Sami, Shital Parekh, Mohamed Ibrahim, Mohammed Raza, Poonam Kapila, and Prithwiraj Chakrabarti. 2021. "Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients" International Journal of Environmental Research and Public Health 18, no. 12: 6228. https://doi.org/10.3390/ijerph18126228

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