Enhancing ALS progression tracking with semi-supervised ALSFRS-R scores estimated from ambient home health monitoring

Abstract

Introduction: Clinical monitoring of functional decline in amyotrophic lateral sclerosis (ALS) relies on periodic assessments, which may miss critical changes that occur between visits when timely interventions are most beneficial. Methods: To address this gap, semi-supervised regression models with pseudo-labeling were developed; these models estimated rates of decline by targeting Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) trajectories with continuous in-home sensor data from a three-patient ALS case series. Three model paradigms were compared (individual batch learning and cohort-level batch vs. incremental fine-tuned transfer learning) across linear slope, cubic polynomial, and ensembled self-attention pseudo-label interpolations. Results: Results showed cohort-level homogeneity across functional domains. For ALSFRS-R subscales, transfer learning reduced the prediction error in 28 of 34 contrasts [mean root mean square error (RMSE) = 0.20 (0.14–0.25)]. However, for composite ALSFRS-R scores, individual batch learning was optimal for two of three participants [mean RMSE = 3.15 (2.24–4.05)]. Self-attention interpolation best captured non-linear progression, providing the lowest subscale-level error [mean RMSE = 0.19 (0.15–0.23)], and outperformed linear and cubic interpolations in 21 of 34 contrasts. Conversely, linear interpolation produced more accurate composite predictions [mean RMSE = 3.13 (2.30–3.95)]. Distinct homogeneity-heterogeneity profiles were identified across domains, with respiratory and speech functions showing patient-specific progression patterns that improved with personalized incremental fine-tuning, while swallowing and dressing functions followed cohort-level trends suited for batch transfer modeling. Discussion: These findings indicate that dynamically matching learning and pseudo-labeling techniques to functional domain-specific homogeneity-heterogeneity profiles enhances predictive accuracy in tracking ALS progression. As an exploratory pilot, these results reflect case-level observations rather than population-wide effects. Integrating adaptive model selection into sensor platforms may enable timely interventions as a method for scalable deployment in future multi-center studies.