Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/93512/ http://psasir.upm.edu.my/id/eprint/93512/1/93512.pdf |
| _version_ | 1848861861917753344 |
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| author | Vepa, Abhinav Saleem, Amer Rakhshan, Kambiz Daneshkhah, Alireza |
| author_facet | Vepa, Abhinav Saleem, Amer Rakhshan, Kambiz Daneshkhah, Alireza |
| author_sort | Vepa, Abhinav |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:07:52Z |
| format | Article |
| id | upm-93512 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:07:52Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-935122024-11-04T04:05:24Z http://psasir.upm.edu.my/id/eprint/93512/ Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients Vepa, Abhinav Saleem, Amer Rakhshan, Kambiz Daneshkhah, Alireza 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. MDPI 2021-06 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/93512/1/93512.pdf Vepa, Abhinav and Saleem, Amer and Rakhshan, Kambiz and Daneshkhah, Alireza (2021) Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients. International Journal of Environmental Research and Public Health, 18 (12). pp. 1-22. ISSN 1661-7827; eISSN: 1660-4601 https://www.mdpi.com/1660-4601/18/12/6228 10.3390/ijerph18126228 |
| spellingShingle | Vepa, Abhinav Saleem, Amer Rakhshan, Kambiz Daneshkhah, Alireza Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients |
| title | Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients |
| title_full | Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients |
| title_fullStr | Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients |
| title_full_unstemmed | Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients |
| title_short | Using machine learning algorithms to develop a clinical 2 decision-making tool for COVID-19 inpatients |
| title_sort | using machine learning algorithms to develop a clinical 2 decision-making tool for covid-19 inpatients |
| url | http://psasir.upm.edu.my/id/eprint/93512/ http://psasir.upm.edu.my/id/eprint/93512/ http://psasir.upm.edu.my/id/eprint/93512/ http://psasir.upm.edu.my/id/eprint/93512/1/93512.pdf |