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

arXiv: https://arxiv.org/abs/1804.03280 · HTML: https://mlsanity.com/paper/1804.03280/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series](https://mlsanity.com/paper/2306.09368/) (2023)
- [SurvNAM: The machine learning survival model explanation](https://mlsanity.com/paper/2104.08903/) (2021)
- [SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator](https://mlsanity.com/paper/2308.03730/) (2023)
- [Bayesian Active Learning for Censored Regression](https://mlsanity.com/paper/2402.11973/) (2024)
- [Large Language Model as a Universal Clinical Multi-task Decoder](https://mlsanity.com/paper/2406.12738/) (2024)
- [SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms](https://mlsanity.com/paper/2412.07638/) (2024)
- [Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization Windows](https://mlsanity.com/paper/2501.14103/) (2025)
- [A new smart-cropping pipeline for prostate segmentation using deep learning networks](https://mlsanity.com/paper/2107.02476/) (2021)
- [SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models](https://mlsanity.com/paper/2312.06638/) (2023)
- [Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables](https://mlsanity.com/paper/2410.13404/) (2024)
- [Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions](https://mlsanity.com/paper/2506.07933/) (2025)

## Abstract (arXiv)

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.