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This study introduces a digital-twin-inspired temporal machine learning framework to predict ALS progression and healthcare utilization by integrating longitudinal ALSFRS-R trajectories with survival modeling. By constructing a harmonized dataset and employing generalized additive mixed models, the researchers identified key predictors of functional decline, particularly emphasizing lower limb function's role in wheelchair access timing. The resulting time-to-event model generates individualized survival curves, offering a scalable and interpretable tool for proactive care planning and clinical decision support in ALS management.
Predicting ALS progression with a digital twin model reveals that lower limb function is the strongest predictor of wheelchair access, transforming how we approach patient care.
Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains challenging. We developed a time-to-event, digital-twin-inspired framework that integrates longitudinal ALS Functional Rating Scale-Revised (ALSFRS-R) trajectories with survival modeling to support individualized prediction of functional decline and assistive device utilization. We constructed a harmonized longitudinal dataset by integrating diagnosis records, ALSFRS-R assessments, activities of daily living, and demographic information, followed by preprocessing to ensure data quality, temporal alignment, and cohort consistency. Correlation-based clustering identified coherent functional domains spanning bulbar, upper limb, axial, lower limb, and respiratory systems. Generalized additive mixed models characterized nonlinear, domain-specific functional decline across all domains. In addition, a temporal machine learning model was developed to predict longitudinal functional decline and capture stage-dependent disease progression. Cox proportional hazards modeling further identified lower limb function, particularly walking and stair climbing, as the strongest predictors of earlier wheelchair access. Building on these results, we implemented a digital twin-inspired temporal machine learning-based time-to-event (TTE) model that generates individualized survival curves and dynamically predicts wheelchair-free survival. This framework provides a scalable, interpretable, and clinically actionable approach for linking ALS progression with personalized decision support, with applications in proactive care planning, clinical trial stratification, and precision medicine.
