The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Random survival forests
Open accessAbstract
We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.
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“Random forests have been extended to the survival context by Ishwaran et al. (2008), who prove consistency of Random Survival Forests (RSF) algorithm assuming that all variables are categorical.”
From A random forest guided tour (2016) · cites this paper as Ishwaran et al. (2008)
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Self-citation
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Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466
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“Another popular machine learning approach to modeling patients’ hazard function is the random survival forest (RSF) [15, 16].”
BackgroundIshwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008) · Cited by 1,310
Self-citationCutler, A., Cutler, D. R., & Stevens, J. R. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7_5 · Cited by 1,313
Metadata
- Authors
- Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S.
- ORCID
- Lauer, Michael S.
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2008-09-01
- Volume
- 2
- Issue
- 3
- Pages
- 841-860
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- OpenAlex
- W2052825782
- Cited by
- 2,714
- References
- 27
- Citations by year
- 2026: 357 · 2025: 341 · 2024: 354 · 2023: 324 · 2022: 296 · 2021: 278 · 2020: 183 · 2019: 148 · 2018: 95 · 2017: 80 · 2016: 75 · 2015: 51 · 2014: 44 · 2013: 25 · 2012: 17
- FWCI
- 14.21
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- random survival forest, right-censored survival data, survival trees, missing data imputation, prognostic model, body mass, coronary artery disease
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