We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential models and thereby supports various survival tasks, such as competing …
We introduce a wide and deep neural network for prediction of progression
from patients with mild cognitive impairment to Alzheimer's disease.
Information from anatomical shape and tabular clinical data (demographics,
biomarkers) are fused in a …
Predictive models for time-to-event data are suitable when only partial information about the outcome is known for a subset of the data – they are censored. Right censoring is the most common form of censoring and is common to clinical studies …
The aim of survival analysis – also referred to as reliability
analysis in engineering – is to analyse the time until one or more
events happen. Examples from the medical domain are the time until
death, until onset of a disease, or until pregnancy. …
Ensemble methods have been successfully applied in a wide range of scenarios,
including survival analysis. However, most ensemble models for survival
analysis consist of models that all optimize the same loss function and do
not fully utilize the …
Survival analysis is a fundamental tool in medical research to identify
predictors of adverse events and develop systems for clinical decision
support. In order to leverage large amounts of patient data, efficient
optimisation routines are paramount. …
Background: In clinical research, the primary interest is often the time
until occurrence of an adverse event, i.e., survival analysis. Its
application to electronic health records is challenging for two main reasons:
1) patient records are comprised …
Survival analysis is a commonly used technique to identify important
predictors of adverse events and develop guidelines for patient's treatment
in medical research. When applied to large amounts of patient data, efficient
optimization routines …