Using machine learning probabilities to identify effects of COVID-19

Ramlall, V., Gisladottir, U., Kefeli, J., Tanaka, Y., May, B., & Tatonetti, N. (2023). Using machine learning probabilities to identify effects of COVID-19. Patterns, 4(12), 100889. https://doi.org/10.1016/j.patter.2023.100889
Authors:
Vijendra Ramlall
Undina Gisladottir
Jenna Kefeli
Yutaro Tanaka
Benjamin May
Nicholas P. Tatonetti
Affiliated Authors:
Vijendra Ramlall
Undina Gisladottir
Jenna Kefeli
Yutaro Tanaka
Benjamin May
Nicholas P. Tatonetti
Columbia Affiliation:
Subjects:
Applications of Deep Learning in Medical Imaging (OpenAlex Topic)
Coronavirus Disease 2019 (OpenAlex Topic)
Modeling the Dynamics of COVID-19 Pandemic (OpenAlex Topic)
Author Keywords:
algorithm development
clinical informatics
covid-19 effects
dsml 2: proof-of-concept: data science output has been formulated, implemented, and tested for one domain/problem
machine learning
survival analysis
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Publication Type:
Article
Unique ID:
10.1016/j.patter.2023.100889
PMID:
38106616
DOI:
10.1016/j.patter.2023.100889
Journal:
Patterns
Publication Date:
Data Source:
OpenAlex
Source Link:
View OpenAlex Record
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Record Created: