[2605.04063] Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis
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Computer Science > Machine Learning
arXiv:2605.04063 (cs)
[Submitted on 10 Apr 2026]
Title:Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis
Authors:Jacob Thrasher, Kaitlyn Heintzelman, Peter Martone, David Kotlowski, Binod Bhattarai, Donald Adjeroh, Prashnna Gyawali
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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.
Comments: 12 pages, 6 figures, 2 tables, IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2605.04063 [cs.LG]
(or arXiv:2605.04063v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.04063
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arXiv-issued DOI via DataCite
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From: Jacob Thrasher [view email] [v1] Fri, 10 Apr 2026 16:41:48 UTC (1,224 KB)
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