Interpretable Prediction and Feature Selection for Survival Analysis

arXiv:2404.14689v1 Announce Type: new
Abstract: Survival analysis is widely used as a technique to model time-to-event data when some data is censored, particularly in healthcare for predicting future patient risk. In such settings, survival models must be both accurate and interpretable so that users (such as doctors) can trust the model and understand model predictions. While most literature focuses on discrimination, interpretability is equally as important. A successful interpretable model should be able to describe how changing each feature impacts …

abstract analysis arxiv cs.lg data doctors event feature feature selection future healthcare patient prediction risk stat.ml survival time-to-event data trust type