COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

Al-Hindawi, Ahmed, Abdulaal, Ahmed, Rawson, Timothy M ORCID: 0000-0002-2630-9722, Alqahtani, Saleh A, Mughal, Nabeela and Moore, Luke SP (2021) COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics. FRONTIERS IN DIGITAL HEALTH, 3. 637944-.

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Official URL: http://dx.doi.org/10.3389/fdgth.2021.637944

Abstract

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and 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 toward the specific task of predicting COVID-19 outcomes.

Item Type:	Article
Uncontrolled Keywords:	COVID-19, Coronavirus, machine learning, artificial intelligence, linear regression
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	28 Mar 2023 09:55
Last Modified:	19 Aug 2023 17:50
DOI:	10.3389/fdgth.2021.637944
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169292