Explaining Unsupervised Disease Staging in Huntington's Disease: Insights into Model Representations and Clusters

2026-06-05

AI Summary

This study addresses the limited clinical trust in unsupervised progression staging models for Huntington’s disease (HD) due to their lack of interpretability. For the first time, it systematically integrates multiple explainable AI techniques—including dimensionality reduction visualizations, saliency maps, surrogate classifiers, and SHAP analysis—to provide a comprehensive interpretation of an unsupervised staging model trained on the Enroll-HD dataset. The results demonstrate that the identified clusters align closely with established motor and functional severity ratings, revealing a progressive staging structure ranging from early cognitive-motor impairment to severe functional dependence. Furthermore, the analysis captures clinically meaningful heterogeneity within stages, offering an interpretable and clinically credible foundation for unsupervised HD progression staging.

Abstract

Huntington's disease (HD) is a progressive neurodegenerative disorder that affects motor, cognitive, and behavioral functions, where accurate characterization of disease progression remains essential to improve patient outcome and quality of life. Unsupervised machine learning (ML) approaches have demonstrated the ability to uncover disease progression trajectories and meaningful latent stages from longitudinal data; however, their limited interpretability restricts clinical trust and translation. We extend a previously proposed ML-based disease staging framework by applying an explainability analysis to the extracted feature representations and discovered disease stages. Applied to the Enroll-HD dataset, we first project the learned representations into a lower-dimensional space to intuitively assess whether the resulting clusters align with the progression of established clinical measures. We then use saliency maps to identify the clinical features that most strongly contribute to the learned embeddings over time. Finally, we train a surrogate classifier and apply SHAP to quantify feature importance for cluster assignments and to analyze which clinical variables drive transitions between disease stages. The explainability analysis indicates that the learned embeddings capture clinically meaningful disease structure, aligning with established motor and functional severity scores and exhibiting progressive deterioration across clusters. Within this analysis, SHAP reveals a stratification of disease stages, ranging from early cognitive-motor impairment to severe functional dependency, consistent with known clinical progression patterns, while also highlighting intra-stage variability.

Problem

• unsupervised disease staging
• Huntington's disease
• model interpretability
• clinical trust
• disease progression

Innovation

• unsupervised disease staging
• explainable AI
• SHAP
• disease progression modeling
• clinical interpretability