A Consensus Clustering Approach to Amyotrophic Lateral Sclerosis Phenotyping

Amyotrophic Lateral Sclerosis (ALS) phenotyping is a challenging task due to its heterogeneous nature and low prevalence. In this paper, we introduce a data-driven approach to support the characterization of ALS phenotypes based on clinical data from a battery of examinations. A consensus clustering method is proposed to identify stable clusters across multiple random data sub-samples, with the objective of discovering whether the retrieved patients' groups and related features align with clinical phenotypes and medical knowledge. Results suggest consistent profiles for bulbar onset ALS patients, driven by onset characteristics, whereas spinal onset ALS patients exhibit greater within-phenotype heterogeneity.