COVID-19 prognostic models: a pro-con debate for machine learning vs traditional statistics
File(s)fdgth-03-637944.pdf (514.46 KB)
Published version
Author(s)
Type
Journal Article
Abstract
The SARS-CoV-2 virus causing the COVID-19 pandemic has had an unprecedented impact on healthcare requiring multi-disciplinary innovation and novel thinking to minimise impact and improve outcomes. Wide ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, critical care) working increasingly closely with data-science. This has been leveraged through the democratisation of data-science with increasing availability of easy to access open datasets, tutorials, programming languages and hardware it is significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modelling of the impact of the virus on the population and on individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method towards the specific task of predicting COVID-19 outcomes
Date Issued
2021-12
Date Acceptance
2021-11-15
Citation
Frontiers in Digital Health, 2021, 3, pp.1-6
ISSN
2673-253X
Publisher
Frontiers Media
Start Page
1
End Page
6
Journal / Book Title
Frontiers in Digital Health
Volume
3
Copyright Statement
© 2021 Al-Hindawi, Abdulaal, Rawson, Alqahtani, Mughal and Moore. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Identifier
https://www.frontiersin.org/articles/10.3389/fdgth.2021.637944/full
Subjects
COVID-19
Coronavirus
artificial intelligence
linear regression
machine learning
Publication Status
Published
Article Number
637944
Date Publish Online
2021-12-23