ArticleInternational journal of environmental research and public health2021
Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- Article
- Article
- Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation.Journal of medical Internet research · 2023Article
- Molecular sensitization pattern to house dust mites is formed from the first years of life and includes group 1, 2, Der p 23, Der p 5, Der p 7 and Der p 21 allergens.Clinical and molecular allergy : CMA · 2023Article
- Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case.Journal of personalized medicine · 2022Article
- COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare.International journal of environmental research and public health · 2021Article
- Comorbidity Patterns of Mood Disorders in Adult Inpatients: Applying Association Rule Mining.Healthcare (Basel, Switzerland) · 2021Article
- Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks.International journal of environmental research and public health · 2021Article
- Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory.International journal of environmental research and public health · 2021Article
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Authors and funding
16 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWithin 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.
methodsAnonymised 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.
resultsThe 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.
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