Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis

TL;DR

This paper evaluates the trustworthiness of deep survival models for Alzheimer's disease progression, focusing on fairness and bias with new metrics and feature importance analysis.

Contribution

It introduces two novel fairness metrics for nonparametric survival models and provides a comprehensive bias analysis in AD progression prediction.

Findings

Deep survival models are effective but exhibit bias towards sensitive attributes.

The study proposes Time-Dependent Concordance Impurity and Kaplan-Meier Fairness metrics.

Bias persists in models despite their robustness, indicating need for fairness improvements.

Abstract

Alzheimer's Dementia (AD) is a progressive neurodegenerative disease marked by irreversible decline, making reliable modeling of its progression essential for effective patient care. Progression-aware methods such as survival analysis are therefore crucial tools for the early detection and monitoring of AD. Recent advancements in deep learning have demonstrated remarkable performance in survival tasks, but alarmingly fewer studies have been conducted in the domain of AD. Further, the studies that do exist do not consider learned bias within the model itself, which could result in unfair and unreliable predictions toward certain marginalized groups. As such, we conduct a rigorous study of fairness in AD progression analysis along with a thorough feature importance study to determine the characteristics which are most important for reliable AD predictions. Furthermore, we propose two novel fairness metrics, called Time-Dependent Concordance Impurity and Kaplan-Meier Fairness, to quantify bias with respect to sensitive attributes such as sex, race, and education in nonparametric survival models. Our study demonstrates that while deep learning powered survival models are robust tools which can aid clinicians in AD care decisions, they often exhibit considerable bias, representing important avenues for future research.