AutoScore_rank_Survival: AutoScore STEP (1) for survival outcomes: Generate variable...

In AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

AutoScore STEP (1) for survival outcomes: Generate variable ranking List by machine learning (Random Survival Forest) (AutoScore Module 1)

Description

AutoScore STEP (1) for survival outcomes: Generate variable ranking List by machine learning (Random Survival Forest) (AutoScore Module 1)

Usage

AutoScore_rank_Survival(train_set, ntree = 50)

Arguments

train_set	A processed data.frame that contains data to be analyzed, for training.
ntree	Number of trees in the random forest (Default: 100).

Details

The first step in the AutoScore framework is variable ranking. We use Random Survival Forest (RSF) for survival outcome to identify the top-ranking predictors for subsequent score generation. This step correspond to Module 1 in the AutoScore-Survival paper.

Value

Returns a vector containing the list of variables and its ranking generated by machine learning (random forest)

References

• Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The annals of applied statistics, 2(3), 841-860.

• Xie F, Ning Y, Yuan H, et al. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J Biomed Inform. 2022;125:103959. doi:10.1016/j.jbi.2021.103959

See Also

AutoScore_parsimony_Survival, AutoScore_weighting_Survival, AutoScore_fine_tuning_Survival, AutoScore_testing_Survival.

Examples

## Not run: 
# see AutoScore-Survival Guidebook for the whole 5-step workflow
data("sample_data_survival") # Output is named `label_time` and `label_status`
ranking <- AutoScore_rank_Survival(sample_data_survival, ntree = 50)

## End(Not run)

AutoScore documentation built on Oct. 16, 2022, 1:06 a.m.