Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness-Aware Machine Learning Models.

ABSTRACT:

Purpose

Prediction models can contribute to disparities in care by performing unequally across demographic groups. While fairness-aware methods have been explored for binary outcomes, applications to survival analysis remain limited. This study compares two fairness-aware deep learning survival models to mitigate racial disparities in predicting survival after radical prostatectomy for prostate cancer.

Methods

We used the National Cancer Database to train deep Cox proportional hazards models for overall survival. Two fairness-aware approaches, Fair Deep Cox Proportional Hazards Model (Fair DCPH) and Group Distributionally Robust Optimization Deep Cox Proportional Hazards Model (GroupDRO DCPH), were compared against a standard Deep Cox model (Baseline). Model fairness was ass

SUBMITTER: Do H 

PROVIDER: S-EPMC12835780 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature