Strategies of Managing Repeated Measures: Using Synthetic Random Forest to Predict HIV Viral Suppression Status Among Hospitalized Persons with HIV

Liu, J., Pan, Y., Nelson, M. C., Gooden, L. K., Metsch, L. R., Rodriguez, A. E., Tross, S., del Rio, C., Mandler, R. N., & Feaster, D. J. (2023). Strategies of Managing Repeated Measures: Using Synthetic Random Forest to Predict HIV Viral Suppression Status Among Hospitalized Persons with HIV. AIDS and Behavior, 27(9), 2915–2931. https://doi.org/10.1007/s10461-023-04015-1
Authors:
Jingxin Liu
Yue Pan
Mindy C Nelson
Lauren K Gooden
Lisa R Metsch
Allan E Rodriguez
Susan Tross
Carlos Del Rio
Raul N Mandler
Daniel J Feaster
Affiliated Authors:
Lauren K Gooden
Lisa R Metsch
Susan Tross
Columbia Affiliation:
Subjects:
HIV Infections (MeSH)
Acquired Immunodeficiency Syndrome (MeSH)
Author Keywords:
hiv/aids
longitudinal measurement
machine learning
predictive model
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Publication Type:
Article
Unique ID:
10.1007/s10461-023-04015-1
PMID:
36739589
DOI:
10.1007/s10461-023-04015-1
Journal:
AIDS and Behavior
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
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Record Created: