A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization

2026-07-15

AI Summary

This study addresses the high heterogeneity in amyotrophic lateral sclerosis (ALS) progression and the difficulty in accurately predicting key clinical milestones such as wheelchair dependence. The authors propose a digital twin–inspired temporal machine learning framework that integrates longitudinal ALSFRS-R scores to dynamically forecast individual functional decline trajectories and time to wheelchair use. By leveraging domain-specific functional correlations through clustering, generalized additive mixed models, and Cox proportional hazards modeling, the approach identifies lower limb function as the strongest predictor of wheelchair dependence. It generates interpretable, patient-specific survival curves, significantly improving both the accuracy and clinical utility of predictions for wheelchair-free survival duration.

Abstract

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.

Problem

• Amyotrophic lateral sclerosis
• time-to-event prediction
• functional decline
• assistive device utilization
• disease progression

Innovation

• temporal machine learning
• digital twin
• time-to-event modeling
• functional decline prediction
• ALS progression