Title: Deep clustering of longitudinal data,
Abstract: Deep neural networks are a family of computational models that have led to a
dramatical improvement of the state of the art in several domains such as
image, voice or text analysis. These methods provide a framework to model
complex, non-linear interactions in large datasets, and are naturally suited to
the analysis of hierarchical data such as, for instance, longitudinal data with
the use of recurrent neural networks. In the other hand, cohort studies have
become a tool of importance in the research field of epidemiology. In such
studies, variables are measured repeatedly over time, to allow the practitioner
to study their temporal evolution as trajectories, and, as such, as
longitudinal data. This paper investigates the application of the advanced
modelling techniques provided by the deep learning framework in the analysis of
the longitudinal data provided by cohort studies. Methods: A method for
visualizing and clustering longitudinal dataset is proposed, and compared to
other widely used approaches to the problem on both real and simulated
datasets. Results: The proposed method is shown to be coherent with the
preexisting procedures on simple tasks, and to outperform them on more complex
tasks such as the partitioning of longitudinal datasets into non-spherical
clusters. Conclusion: Deep artificial neural networks can be used to visualize
longitudinal data in a low dimensional manifold that is much simpler to
interpret than traditional longitudinal plots are. Consequently, practitioners
should start considering the use of deep artificial neural networks for the
analysis of their longitudinal data in studies to come.