Using machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatients

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dc.contributor.authorVepa, Abhinav
dc.contributor.authorSaleem, Amer
dc.contributor.authorRakhshan, Kambiz
dc.contributor.authorDaneshkhah, Alireza
dc.contributor.authorSedighi, Tabassom
dc.contributor.authorShohaimi, Shamarina
dc.contributor.authorOmar, Amr
dc.contributor.authorSalari, Nader
dc.contributor.authorChatrabgoun, Omid
dc.contributor.authorDharmaraj, Diana
dc.contributor.authorSami, Junaid
dc.contributor.authorParekh, Shital
dc.contributor.authorIbrahim, Mohamed
dc.contributor.authorRaza, Mohammed
dc.contributor.authorKapila, Poonam
dc.contributor.authorChakrabarti, Prithwiraj
dc.date.accessioned2021-06-18T14:45:52Z
dc.date.available2021-06-18T14:45:52Z
dc.date.issued2021-06-09
dc.description.abstractBackground: 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.en_UK
dc.identifier.citationVepa A, Saleem A, Rakhshan K, et al., (2021) Using machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatients. International Journal of Environmental Research and Public Health, Volume 18, Issue 12, June 2021, Article number 6228en_UK
dc.identifier.issn1661-7827
dc.identifier.urihttps://doi.org/10.3390/ijerph18126228
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16788
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBayesian networken_UK
dc.subjectCOVID-19en_UK
dc.subjectSARS CoVen_UK
dc.subjectrandom foresten_UK
dc.subjectrisk stratificationen_UK
dc.subjectsynthetic minority oversampling technique (SMOTE)en_UK
dc.titleUsing machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatientsen_UK
dc.typeArticleen_UK

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