Amyotrophic Lateral Sclerosis is a devastating neurodegenerative
disease characterized by a usually fast progression of
muscular denervation, generally leading to death in a few
years from onset. In this context, any significant improvement
of the patient's life expectancy and quality is of major
relevance. Several studies have been made to address problems
such as ALS diagnosis, and more recently, prognosis.
However, these analysis have been mostly restricted to classical
statistical approaches used to find the most associated
features to a given outcome of interest. In this work we explore
an innovative approach to the analysis of clinical data characterized
by multivariate time series. We use a distance measure
between patients as a reflection of their relationship, to
build a patients network, which in turn can be studied from
a modularity point of view, in order to search for communities,
or groups of similar patients. The preliminary results
show that it is possible to extract relevant information from
such groups, each presenting a particular behavior for some
of the features (patient characteristics) under analysis.