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Found 8 papers, 3 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.
Scoring rules in survival analysis
In this paper we survey proposed scoring rules for survival analysis, establish the first clear definition of `(strict) properness' for survival scoring rules, and determine which losses are proper and improper.
Flexible Group Fairness Metrics for Survival Analysis
In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics.
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination.
Designing Machine Learning Toolboxes: Concepts, Principles and Patterns
In particular, we develop a conceptual model for the AI/ML domain, with a new type system, called scientific types, at its core.
mlr3proba: An R Package for Machine Learning in Survival Analysis
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models.
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature.
