Learning a Distance for the Clustering of Patients with Amyotrophic Lateral Sclerosis

TL;DR

This paper introduces a novel clustering method for ALS patient data that leverages a disease progression score and learned distances, improving survival prediction and interpretability over existing techniques.

Contribution

It proposes a new clustering approach combining medical expertise with weak-supervised learning to improve patient subgrouping in ALS research.

Findings

Outperforms state-of-the-art clustering methods in survival analysis.

Achieves comparable silhouette scores to existing methods.

Enhances interpretability of patient clusters for medical experts.

Abstract

Amyotrophic lateral sclerosis (ALS) is a severe disease with a typical survival of 3-5 years after symptom onset. Current treatments offer only limited life extension, and the variability in patient responses highlights the need for personalized care. However, research is hindered by small, heterogeneous cohorts, sparse longitudinal data, and the lack of a clear definition for clinically meaningful patient clusters. Existing clustering methods remain limited in both scope and number. To address this, we propose a clustering approach that groups sequences using a disease progression declarative score. Our approach integrates medical expertise through multiple descriptive variables, investigating several distance measures combining such variables, both by reusing off-the-shelf distances and employing a weak-supervised learning method. We pair these distances with clustering methods and benchmark them against state-of-the-art techniques. The evaluation of our approach on a dataset of 353 ALS patients from the University Hospital of Tours, shows that our method outperforms state-of-the-art methods in survival analysis while achieving comparable silhouette scores. In addition, the learned distances enhance the relevance and interpretability of results for medical experts.