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.

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Cited by in corpus (7)

• Deep Learning for Survival Analysis: A Review
• Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
• Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
• SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
• Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery
• Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
• A new smart-cropping pipeline for prostate segmentation using deep learning networks