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

Abhinav Vepa, 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 and Prithwiraj Chakrabarti
Additional contact information
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

IJERPH, 2021, vol. 18, issue 12, 1-22

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.

Keywords: Bayesian network; COVID-19; SARS CoV; random forest; risk stratification; synthetic minority oversampling technique (SMOTE) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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