Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.

ABSTRACT:

Background

Amyotrophic lateral sclerosis (ALS) is known to represent a collection of overlapping syndromes. Various classification systems based on empirical observations have been proposed, but it is unclear to what extent they reflect ALS population substructures. We aimed to use machine-learning techniques to identify the number and nature of ALS subtypes to obtain a better understanding of this heterogeneity, enhance our understanding of the disease, and improve clinical care.

Methods

In this retrospective study, we applied unsupervised Uniform Manifold Approximation and Projection [UMAP]) modelling, semi-supervised (neural network UMAP) modelling, and supervised (ensemble learning based on LightGBM) modelling to a population-based discovery cohort of patients who were d

SUBMITTER: Faghri F 

PROVIDER: S-EPMC9038712 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature