Machine learning for survival analysis: A survey
Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …
where the outcome is the time until an event of interest occurs. One of the main challenges …
[HTML][HTML] Deep learning for survival analysis: a review
The influx of deep learning (DL) techniques into the field of survival analysis in recent years
has led to substantial methodological progress; for instance, learning from unstructured or …
has led to substantial methodological progress; for instance, learning from unstructured or …
Time-to-event prediction with neural networks and Cox regression
New methods for time-to-event prediction are proposed by extending the Cox proportional
hazards model with neural networks. Building on methodology from nested case-control …
hazards model with neural networks. Building on methodology from nested case-control …
[HTML][HTML] Long-term cancer survival prediction using multimodal deep learning
LA Vale-Silva, K Rohr - Scientific Reports, 2021 - nature.com
The age of precision medicine demands powerful computational techniques to handle high-
dimensional patient data. We present MultiSurv, a multimodal deep learning method for long …
dimensional patient data. We present MultiSurv, a multimodal deep learning method for long …
Deephit: A deep learning approach to survival analysis with competing risks
Survival analysis (time-to-event analysis) is widely used in economics and finance,
engineering, medicine and many other areas. A fundamental problem is to understand the …
engineering, medicine and many other areas. A fundamental problem is to understand the …
[HTML][HTML] DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Background Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (eg clinical and genetic features) and the …
relationships between patients' covariates (eg clinical and genetic features) and the …
[HTML][HTML] Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data
T Ching, X Zhu, LX Garmire - PLoS computational biology, 2018 - journals.plos.org
Artificial neural networks (ANN) are computing architectures with many interconnections of
simple neural-inspired computing elements, and have been applied to biomedical fields …
simple neural-inspired computing elements, and have been applied to biomedical fields …
Deep-learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of moving objects in
sequences of images. Optimizing the interpretation of dynamic biological systems requires …
sequences of images. Optimizing the interpretation of dynamic biological systems requires …
[图书][B] Bayesian survival analysis
Several topics are addressed, including parametric models, semiparametric models based
on prior processes, proportional and non-proportional hazards models, frailty models, cure …
on prior processes, proportional and non-proportional hazards models, frailty models, cure …
[HTML][HTML] SurvSHAP (t): time-dependent explanations of machine learning survival models
Abstract Machine and deep learning survival models demonstrate similar or even improved
time-to-event prediction capabilities compared to classical statistical learning methods yet …
time-to-event prediction capabilities compared to classical statistical learning methods yet …