Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study.
Coronavirus
Machine learning
Pneumonia
Predictive model
SARS-CoV-2
Journal
PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425
Informations de publication
Date de publication:
2021
2021
Historique:
received:
12
01
2021
accepted:
11
03
2021
entrez:
12
5
2021
pubmed:
13
5
2021
medline:
13
5
2021
Statut:
epublish
Résumé
This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (
Identifiants
pubmed: 33976972
doi: 10.7717/peerj.11205
pii: 11205
pmc: PMC8061580
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e11205Informations de copyright
© 2021 Lu et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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