How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
Authors
van der Schaar, Mihaela
Alaa, Ahmed M.
Floto, Andres
Gimson, Alexander
Scholtes, Stefan
Wood, Angela
McKinney, Eoin
Jarrett, Daniel
Lio, Pietro
Ercole, Ari
Publication Date
2020-12-09Journal Title
Machine Learning
ISSN
0885-6125
Publisher
Springer US
Volume
110
Issue
1
Pages
1-14
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., McKinney, E., et al. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110 (1), 1-14. https://doi.org/10.1007/s10994-020-05928-x
Description
Funder: University of Cambridge
Abstract
Abstract: The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.
Keywords
Article, Clinical decision support, Healthcare, COVID-19
Identifiers
s10994-020-05928-x, 5928
External DOI: https://doi.org/10.1007/s10994-020-05928-x
This record's URL: https://www.repository.cam.ac.uk/handle/1810/316652
Rights
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/
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