Editing Survival analysis (section)

===Deep Learning survival models===

Recent advancements in deep representation learning have been extended to survival estimation. The DeepSurv<ref>{{cite journal |last1=Singh |first1=Jared |last2= Katzman |first2=L. |title= DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network |journal=[[BMC Medical Research Methodology]] |year=2018}}</ref> model proposes to replace the log-linear parameterization of the CoxPH model with a multi-layer perceptron. Further extensions like Deep Survival Machines<ref>{{cite journal |last=Nagpal |first=Chirag |title=Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks. |journal= IEEE Journal of Biomedical and Health Informatics |volume=25 |year=2021 |issue=8|pages=3163–3175 |doi=10.1109/JBHI.2021.3052441 |pmid=33460387 |arxiv=2003.01176 |s2cid=211817982 }}</ref> and Deep Cox Mixtures<ref>{{cite journal |last=Nagpal |first=Chirag |title= Deep Cox mixtures for survival regression. |journal= Machine Learning for Healthcare Conference |year=2021 |arxiv=2101.06536 }}</ref> involve the use of latent variable mixture models to model the time-to-event distribution as a mixture of parametric or semi-parametric distributions while jointly learning representations of the input covariates. Deep learning approaches have shown superior performance especially on complex input data modalities such as images and clinical time-series.