Dynamic modelling of the ALSFRS-R: leveraging population-based scores using neural networks.

ABSTRACT:

Background

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disorder with highly heterogeneous trajectories. The Revised ALS Functional Rating Scale (ALSFRS-R) is challenging to model due to irregularly spaced data and patient-level variability. Here we sought to develop and validate a short-horizon prediction tool leveraging a fully connected neural network (FCNN) to forecast individual ALSFRS-R trajectories, providing a natural history benchmark for trials and clinical practice.

Methods

We retrospectively analysed 29,992 ALSFRS-R measurements from 5319 people living with ALS (plwALS) in the population-based PRECISION-ALS dataset. plwALS were randomised (80:20) into a training and test cohort using group-based splitting. A three-layer FCNN was

SUBMITTER: McFarlane R 

PROVIDER: S-EPMC12664354 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature