Statistical and machine learning methods for survival data: prediction, performance assessment and interpretability

Project Summary

(Former title: Machine Learning for Survival Data Prediction with applications to Primary Immunodeficiences data)

This PhD thesis is at the frontier between mathematics and data science. Its aim is to develop new statistical and machine learning methods for survival analysis with a focus on the prediction of the time to event rather than on the traditional estimation of hazard rates. We build a comprehensive analysis framework for the prediction of the time to event from right-censored data, including a new prediction method as well as new criteria to assess the performance and to enhance the interpretability of such predictors.