SRF-CLICAL: an approach for patient risk stratification using random forest models

Abstract

An important part of good clinical care is identifying which patients have a high likelihood of experiencing adverse outcomes. Similarly, due to the significant impact cancer treatment can have on a patients quality of life, it is also important to properly identify which patients are likely to benefit from more aggressive treatment options. As such, models for predictive risk stratification can be extremely useful in clinical decision making. In this paper, we present, Survival Random Forest-Clinical Categorization Algorithm (SRF-CLICAL), a new method for patient risk stratification using random forests for survival, regression and classification. As a proof of concept, we demonstrate this method on two different cohorts of cancer patients.