A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer

TL;DR

This paper introduces a deep active learning framework for survival analysis in healthcare, specifically for prostate cancer, improving prediction accuracy with limited labeled data and aiding treatment recommendations.

Contribution

It presents a novel deep learning and active learning-based survival analysis method that effectively utilizes both labeled and unlabeled clinical data for better predictions.

Findings

Outperforms baseline models significantly in survival prediction.

Provides a lower-dimensional, better representation of high-dimensional clinical data.

Enables effective treatment recommendations based on survival analysis.

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.