New paper accepted

A new paper from the group has been accepted for publication

The paper “Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders” by Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco and Fernando Perez-Cruz has been accepted for publication by the Journal of Machine Learning Research. (PDF)
The analysis of comorbidity is an open and complex research eld in the branch of psychiatry,
where clinical experience and several studies suggest that the relation among the
psychiatric disorders may have etiological and treatment implications. In this paper, we
are interested in applying latent feature modeling to nd the latent structure behind the
psychiatric disorders that can help to examine and explain the relationships among them.
To this end, we use the large amount of information collected in the National Epidemiologic
Survey on Alcohol and Related Conditions (NESARC) database and propose to model these
data using a nonparametric latent model based on the Indian Bu et Process (IBP). Due
to the discrete nature of the data, we rst need to adapt the observation model for discrete
random variables. We propose a generative model in which the observations are drawn from
a multinomial-logit distribution given the IBP matrix. The implementation of an ecient
Gibbs sampler is accomplished using the Laplace approximation, which allows integrating
out the weighting factors of the multinomial-logit likelihood model. We also provide a
variational inference algorithm for this model, which provides a complementary (and less
expensive in terms of computational complexity) alternative to the Gibbs sampler allowing
us to deal with a larger number of data. Finally, we use the model to analyze comorbidity
among the psychiatric disorders diagnosed by experts from the NESARC database.