Model-agnostic explanations for survival prediction models.

Area of Science:

• Biomedical research
• Machine learning
• Survival analysis

Background:

• Advanced machine learning models accurately predict time-to-event outcomes in biomedical research.
• These complex models are often criticized as "black boxes," lacking interpretability and hindering clinical trust.
• Explainable machine learning (XML) aims to address this by providing insights into model predictions.

Purpose of the Study:

• To present a model-agnostic approach for generating explanations from survival prediction models.
• To extend the Local Interpretable Model-agnostic Explainer (LIME) framework for survival outcomes.
• To provide explanations for both specific survival predictions and overall patient survival curves.

Main Methods:

• Developed a model-agnostic explainer framework adaptable to any survival prediction model.
• Extended the LIME framework, originally for classification, to accommodate survival prediction tasks.
• Utilized simulated data to evaluate the performance of the proposed explanation methods.

Main Results:

• The proposed model-agnostic approach successfully generates explanations for survival predictions.
• The methodology provides insights into how individual patient characteristics influence survival predictions.
• Demonstrated the application of the new explainable AI (XAI) methods using prostate cancer data.

Conclusions:

• The presented model-agnostic explainability framework enhances the interpretability of survival prediction models.
• This approach can increase trust and facilitate the clinical adoption of advanced machine learning in healthcare.
• The methodology offers valuable tools for understanding patient-specific survival predictions and overall survival curves.