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Found 5 papers, 2 papers with code
Training Survival Models using Scoring Rules
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains.
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 high-dimensional data such as images, text or omics data.
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications.
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.
