Medical Survival Analysis Through Transduction of Semi-Supervised Regression Targets

Abstract

A crucial challenge in predictive modeling for survival analysis applications such as medical prognosis is the accounting of censored observations in the data. While these time-to-event predictions inherently represent a regression problem, traditional regression approaches are challenged by the censored characteristics of the data. In such problems the true target times of a majority of instances are unknown; what is known is a censored target representing some indeterminate time before the true target time. While censored samples can be considered as semi-supervised targets, the current limited efforts in semi-supervised regression do not take into account the partial nature of unsupervised information; samples are treated as either fully labeled or unlabelled. This paper presents a novel semi-supervised learning approach where the true target times are approximated from the censored times through transduction. The method can be employed to transform traditional regression methods for survival analysis, or can be employed to enhance existing state-of-the-art survival analysis methods for improved predictive performance. The proposed approach represents one of the first applications of semi-supervised regression to survival analysis and yields a significant improvement in performance over the state-of-the-art in prostate and breast cancer prognosis applications.