survex

survex provides explainable artificial intelligence (XAI) techniques for machine learning survival models to elucidate variable effects and prediction rationales in survival analysis.

Key Features:

• Explainability Framework: Applies explainable artificial intelligence (XAI) techniques tailored for survival analysis to elucidate the decision-making processes of survival models.
• Variable Effects and Importances: Enables assessment of variable effects and importances within models to show how input covariates influence predicted survival outcomes.
• Model Reliability Assessment: Exposes internal model operations and prediction drivers to support evaluation of model reliability across scenarios.
• Bias Detection: Identifies potential biases within survival models to inform fairness and accuracy analyses.

Scientific Applications:

• Biomedical Research: Interprets survival models in domains such as oncology and epidemiology to inform understanding of patient outcomes over time.
• Healthcare Applications: Supports personalized medicine and treatment planning by providing transparent and interpretable survival predictions.

Methodology:

Applies explainable artificial intelligence (XAI) techniques to dissect and present the inner workings of machine learning survival models by breaking down complex algorithms into components that highlight how input variables affect model outcomes.