Title: A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer,
Abstract: Survival analysis has been developed and applied in the number of areas
including manufacturing, finance, economics and healthcare. In healthcare
domain, usually clinical data are high-dimensional, sparse and complex and
sometimes there exists few amount of time-to-event (labeled) instances.
Therefore building an accurate survival model from electronic health records is
challenging. With this motivation, we address this issue and provide a new
survival analysis framework using deep learning and active learning with a
novel sampling strategy. First, our approach provides better representation
with lower dimensions from clinical features using labeled (time-to-event) and
unlabeled (censored) instances and then actively trains the survival model by
labeling the censored data using an oracle. As a clinical assistive tool, we
introduce a simple effective treatment recommendation approach based on our
survival model. In the experimental study, we apply our approach on
SEER-Medicare data related to prostate cancer among African-Americans and white
patients. The results indicate that our approach outperforms significantly than
baseline models.