Machine learning to assist clinical decision-making during the COVID-19 pandemic.
Artificial intelligence (AI)
Clinical decision-making
Coronavirus disease 19 (COVID-19)
Healthcare
Machine learning (ML)
Journal
Bioelectronic medicine
ISSN: 2332-8886
Titre abrégé: Bioelectron Med
Pays: England
ID NLM: 101660849
Informations de publication
Date de publication:
2020
2020
Historique:
received:
30
04
2020
accepted:
08
06
2020
entrez:
16
7
2020
pubmed:
16
7
2020
medline:
16
7
2020
Statut:
epublish
Résumé
The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
Sections du résumé
BACKGROUND
BACKGROUND
The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information.
MAIN BODY
METHODS
While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models.
CONCLUSION
CONCLUSIONS
This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
Identifiants
pubmed: 32665967
doi: 10.1186/s42234-020-00050-8
pii: 50
pmc: PMC7347420
doi:
Types de publication
Journal Article
Langues
eng
Pagination
14Subventions
Organisme : NLM NIH HHS
ID : R01 LM012836
Pays : United States
Organisme : NIA NIH HHS
ID : R24 AG064191
Pays : United States
Investigateurs
Lance B Becker
(LB)
Jennifer Cookingham
(J)
Karina W Davidson
(KW)
Andrew J Dominello
(AJ)
Louise Falzon
(L)
Thomas McGinn
(T)
Jazmin N Mogavero
(JN)
Gabrielle A Osorio
(GA)
Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
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