Cited by 2,714Statistical Methods and InferenceProbability and Statistical ResearchArtificial Intelligence in Healthcare

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Random survival forests

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Ishwaran, Hemant, Kogalur, Udaya B., Blackstone, Eugene H., Lauer, Michael S.·2008·The Annals of Applied Statistics

DOI 10.1214/08-aoas169

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Abstract

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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001
Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S. (2003) · JAMA · doi:10.1001/jama.289.1.76 · Cited by 5,866
Excess Deaths Associated With Underweight, Overweight, and Obesity
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A Potential Decline in Life Expectancy in the United States in the 21st Century
Olshansky, S. J., Passaro, D. J., Hershow, R. C., Layden, J., Carnes, B. A., Brody, J., Hayflick, L., Butler, R. N., Allison, D. B., & Ludwig, D. S. (2005) · New England Journal of Medicine · doi:10.1056/nejmsr043743 · Cited by 2,612
Boosting the margin: a new explanation for the effectiveness of voting methods
Bartlett, P. L., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,574
Years of Life Lost Due to Obesity
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Overweight, Obesity, and Mortality in a Large Prospective Cohort of Persons 50 to 71 Years Old
Adams, K. F., Schatzkin, A. G., Harris, T. B., Kipnis, V., Mouw, T., Ballard-Barbash, R. R., Hollenbeck, A. R., & Leitzmann, M. F. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa055643 · Cited by 2,147

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Fleming, T. and Harrington, D. (1991). Counting Processes and Survival Analysis. Wiley, New York.
Kalbfleisch, J. and Prentice, R. (1980). The Statistical Analysis of Failure Time Data. Wiley, New York.
Rossi, P. H., Berk, R. A. and Lenihan, K. J. (1980). Money, Work and Crime: Some Experimental Results. Academic Press, New York.

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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
DOI: 10.1214/08-aoas169
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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