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

Author

Listed:
  • Abhinav Vepa

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Amer Saleem

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Kambiz Rakhshan

    (Leeds Sustainability Institute, Leeds Beckett University, Leeds LS1 3HE, UK)

  • Alireza Daneshkhah

    (Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 5FB, UK)

  • Tabassom Sedighi

    (Centre for Environment and Agricultural Informatics, Cranfield University, Bedfordshire MK43 0AL, UK)

  • Shamarina Shohaimi

    (Department of Biology, Faculty of Science, University Putra Malaysia, Serdang, Selangor 43400, Malaysia)

  • Amr Omar

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Nader Salari

    (Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran)

  • Omid Chatrabgoun

    (Faculty of Mathematical Sciences & Statistics, Malayer University, Malayer 6571995863, Iran)

  • Diana Dharmaraj

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Junaid Sami

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Shital Parekh

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Mohamed Ibrahim

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Mohammed Raza

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Poonam Kapila

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

  • Prithwiraj Chakrabarti

    (Milton Keynes University Hospital, Standing Way, Eaglestone, Milton Keynes MK6 5LD, UK)

Abstract

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.

Suggested Citation

  • Abhinav Vepa & Amer Saleem & Kambiz Rakhshan & Alireza Daneshkhah & Tabassom Sedighi & Shamarina Shohaimi & Amr Omar & Nader Salari & Omid Chatrabgoun & Diana Dharmaraj & Junaid Sami & Shital Parekh &, 2021. "Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients," IJERPH, MDPI, vol. 18(12), pages 1-22, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6228-:d:571521
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    References listed on IDEAS

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    1. William McGill, 1954. "Multivariate information transmission," Psychometrika, Springer;The Psychometric Society, vol. 19(2), pages 97-116, June.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
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    Cited by:

    1. Debaditya Shome & T. Kar & Sachi Nandan Mohanty & Prayag Tiwari & Khan Muhammad & Abdullah AlTameem & Yazhou Zhang & Abdul Khader Jilani Saudagar, 2021. "COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
    2. Finn Stevenson & Kentaro Hayasi & Nicola Luigi Bragazzi & Jude Dzevela Kong & Ali Asgary & Benjamin Lieberman & Xifeng Ruan & Thuso Mathaha & Salah-Eddine Dahbi & Joshua Choma & Mary Kawonga & Mduduzi, 2021. "Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    3. Tabassom Sedighi & Liz Varga & Amin Hosseinian-Far & Alireza Daneshkhah, 2021. "Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks," IJERPH, MDPI, vol. 18(14), pages 1-16, July.

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