Deep Survival Analysis

By Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei. 2016

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Several attempts [84,86] at recasting the clinical decision-making problem into a prediction problem (i.”
A Survey on Deep Learning (2018, background): “Nonetheless, there are still limitations and challenges that require attention from the research community [131].”
Variational Sequential Monte Carlo (2017, methodology): “Recent advances in these challenging domains have relied upon variational inference ( VI ) [Kingma and Welling, 2014, Hoffman et al., 2013, Ranganath et al., 2016a].”
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, methodology): “Applications of deep learning for other tasks in healthcare have shown promising results; examples include mortality prediction [22], patient note de-identification [23], skin cancer detection [24], and diabetic retinopathy detection [25].”
Machine Learning for Survival Analysis: A Survey (2017, background): “Several deep survival analysis approaches [Katzman et al. 2016; Ranganath et al. 2016] are also proposed to assist the physicians with their clinical decisions on the patients by estimating their risk of diseases and provide personalized treatment recommendations.”
SurvNAM: The machine learning survival model explanation (2021, methodology): “Many models have been developed to relax the linear relationship assumption accepted in the Cox model [57, 58, 59, 60, 61, 62].”
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction (2017, background): “[39] use deep exponential families [40] to develop a latent representation of diverse, multivariate data (e.”
A Review of Challenges and Opportunities in Machine Learning for Health. (2018, background): “For example, inferring drug-resistance status in tuberculosis from whole-genome sequencing data 103 , predicting cardiovascular risk from EHR data 64,104 , readmission and mortality risk from longitudinal EHR data 39,105 , and predicting cardiovascular risk from retinal images 106 .”
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network (2016, background): “A more recent deep learning approach models the event time according to a Weibull distribution with parameters given by latent variables generated by a deep exponential family [17].”
Disease-Atlas: Navigating Disease Trajectories using Deep Learning (2018, background): “Incorporating Medical History into Forecasts While deep learning for medicine has gained popularity in recent times, the majority of methods, such as (Alaa and van der Schaar, 2017; Ranganath et al., 2016), only use covariates at a single time point in making predictions.”