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

Abhinav Veoa, 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, Prithwiraj Chakrabarti

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    15 Citations (Scopus)
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    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.
    Original languageEnglish
    Article number6228
    Number of pages22
    JournalInternational Journal of Environmental Research and Public Health
    Issue number12
    Publication statusPublished - 9 Jun 2021

    Bibliographical note

    This article is an open access article distributed under the terms and
    conditions of the Creative Commons Attribution (CC BY) license (


    • bayesian network
    • COVID-19
    • Random forest
    • SARS-CoV-2
    • risk stratification
    • synthetic minority oversampling technique
    • Synthetic minority oversampling technique (SMOTE)
    • Risk stratification
    • SARS CoV
    • Bayesian network

    ASJC Scopus subject areas

    • Public Health, Environmental and Occupational Health
    • Pollution
    • Health, Toxicology and Mutagenesis


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