Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission

arXiv:2403.06999v1 Announce Type: new
Abstract: Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art machine learning algorithms, support healthcare intervention and policy decisions. However, there remains ongoing discussion about their comparative performance. We conducted a comparative study of several survival analysis methods, including Cox proportional hazards (CoxPH), stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests …

abstract algorithms analysis art arxiv comparative analysis cs.ai cs.cy cs.lg deep learning dynamic event hospital machine machine learning machine learning algorithms modeling mortality probability state statistical studying support survival type understanding