A review on longitudinal data analysis with random forest.
clustered data
longitudinal data
machine learning
multivariate response
repeated measurements
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
19 03 2023
19 03 2023
Historique:
received:
31
08
2022
revised:
12
12
2022
accepted:
31
12
2012
pubmed:
19
1
2023
medline:
22
3
2023
entrez:
18
1
2023
Statut:
ppublish
Résumé
In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.
Identifiants
pubmed: 36653905
pii: 6991123
doi: 10.1093/bib/bbad002
pmc: PMC10025446
pii:
doi:
Types de publication
Review
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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