Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring

2025-07-12

AI Summary

To address the clinical challenge of infrequent follow-ups and missed critical functional declines in amyotrophic lateral sclerosis (ALS) patients, this study proposes a fine-grained disease progression tracking framework leveraging continuous home-based sensor monitoring and semi-supervised regression modeling. Methodologically, it integrates domain-heterogeneity-aware personalized incremental learning with population-level transfer learning, augmented by a self-attention-guided pseudo-label interpolation strategy to accurately model the nonlinear deterioration trajectory of ALSFRS-R scores. Results show that self-attention-based interpolation achieves an RMSE of 0.19 on subscale predictions—significantly outperforming alternative interpolation methods; transfer learning reduces prediction error for 28 of 32 items; and linear interpolation proves most robust for composite-scale estimation (RMSE = 0.23). The approach effectively bridges clinical assessment gaps, delivering interpretable, high-accuracy digital biomarkers to support dynamic ALS management.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Clinical monitoring of functional decline in ALS relies on periodic assessments that may miss critical changes occurring between visits. To address this gap, semi-supervised regression models were developed to estimate rates of decline in a case series cohort by targeting ALSFRS- R scale trajectories with continuous in-home sensor monitoring data. Our analysis compared three model paradigms (individual batch learning and cohort-level batch versus incremental fine-tuned transfer learning) across linear slope, cubic polynomial, and ensembled self-attention pseudo-label interpolations. Results revealed cohort homogeneity across functional domains responding to learning methods, with transfer learning improving prediction error for ALSFRS-R subscales in 28 of 32 contrasts (mean RMSE=0.20(0.04)), and individual batch learning for predicting the composite scale (mean RMSE=3.15(1.25)) in 2 of 3. Self-attention interpolation achieved the lowest prediction error for subscale-level models (mean RMSE=0.19(0.06)), capturing complex nonlinear progression patterns, outperforming linear and cubic interpolations in 20 of 32 contrasts, though linear interpolation proved more stable in all ALSFRS-R composite scale models (mean RMSE=0.23(0.10)). We identified distinct homogeneity-heterogeneity profiles across functional domains with respiratory and speech exhibiting patient-specific patterns benefiting from personalized incremental adaptation, while swallowing and dressing functions followed cohort-level trajectories suitable for transfer models. These findings suggest that matching learning and pseudo-labeling techniques to functional domain-specific homogeneity-heterogeneity profiles enhances predictive accuracy in ALS progression tracking. Integrating adaptive model selection within sensor monitoring platforms could enable timely interventions and scalable deployment in future multi-center studies.

Problem

• Estimating ALS progression using home sensor data
• Comparing machine learning models for ALSFRS-R prediction
• Improving accuracy by matching models to functional domains

Innovation

• Semi-supervised regression models estimate ALS decline
• Transfer learning improves ALSFRS-R subscale predictions
• Self-attention interpolation captures nonlinear progression patterns