survival-analysis

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

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 …

Models for time-to-event data – From Cox's proportional hazards model to deep learning

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 …

Introduction to Survival Analysis with scikit-survival

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. …

Heterogeneous Ensembles for Predicting Survival of Metastatic, Castrate-Resistant Prostate Cancer Patients

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 …

An Efficient Training Algorithm for Kernel Survival Support Vector Machines

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. …

Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection

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 …

Fast Training of Support Vector Machines for Survival Analysis

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 …