How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.
COVID-19
Clinical decision support
Healthcare
Journal
Machine learning
ISSN: 0885-6125
Titre abrégé: Mach Learn
Pays: United States
ID NLM: 9881780
Informations de publication
Date de publication:
2021
2021
Historique:
received:
19
07
2020
revised:
18
10
2020
accepted:
21
10
2020
pubmed:
16
12
2020
medline:
16
12
2020
entrez:
15
12
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33318723
doi: 10.1007/s10994-020-05928-x
pii: 5928
pmc: PMC7725494
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1-14Subventions
Organisme : British Heart Foundation
ID : SP/18/3/33801
Pays : United Kingdom
Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Conflict of interestThe authors declare that they have no conflict of interest.
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