Risk prediction is a type of supervised learning where the goal is to predict the probability that a person will experience an event within a specific amount of time. This kind of prediction may be useful in clinical settings, where identifying patients who are at high risk for experiencing an adverse health outcome can help guide strategies for prevention and treatment. The oblique random survival forest (RSF) is a supervised learning technique that has obtained high prediction accuracy in general benchmarks for risk prediction (Jaeger et al., 2019). However, computational overhead and a lack of tools for interpretation make it difficult to use the oblique RSF in applied settings.
Paper
Full text
aorsf: An R package for supervised learning using the oblique random survival forest
Semantic Scholar · Computer Science · 2022
Abstract
Risk prediction is a type of supervised learning where the goal is to predict the probability that a person will experience an event within a specific amount of time. This kind of prediction may be useful in clinical settings, where identifying patients who are at high risk for experiencing an adverse health outcome can help guide strategies for prevention and treatment. The oblique random survival forest (RSF) is a supervised learning technique that has obtained high prediction accuracy in general benchmarks for risk prediction (Jaeger et al., 2019). However, computational overhead and a lack of tools for interpretation make it difficult to use the oblique RSF in applied settings.