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Bayesian nonparametric comorbidity analysis of psychiatric disorders

Editors: Kevin Murphy, Bernhard Schölkopf Authors:

Francisco J. R. Ruiz,

Fernando Perez-CruzAuthors Info & Claims

The Journal of Machine Learning Research, Volume 15, Issue 1

Pages 1215 - 1247

Published: 01 January 2014 Publication History

Abstract

The analysis of comorbidity is an open and complex research field 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 find 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 Buffet Process (IBP). Due to the discrete nature of the data, we first 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 effcient 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.

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Cited By

Pradier MStojkoski VUtkovski ZKocorev LPerez-Cruz F(2018)Sparse Three-Parameter Restricted Indian Buffet Process for Understanding International Trade2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8462666(2476-2480)Online publication date: 15-Apr-2018
https://dl.acm.org/doi/10.1109/ICASSP.2018.8462666
Doshi-Velez FWilliamson S(2017)Restricted Indian buffet processesStatistics and Computing10.1007/s11222-016-9681-y27:5(1205-1223)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11222-016-9681-y
Vellanki PDuong TGupta SVenkatesh SPhung D(2017)Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic dataKnowledge and Information Systems10.1007/s10115-016-0971-751:1(127-157)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s10115-016-0971-7
Show More Cited By

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Bayesian nonparametric comorbidity analysis of psychiatric disorders

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Published In

cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 15, Issue 1

January 2014

4085 pages

ISSN:1532-4435

EISSN:1533-7928

Editors:
Kevin Murphy
Google
,
Bernhard Schölkopf

Issue’s Table of Contents

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JMLR.org

Publication History

Published: 01 January 2014

Revised: 01 December 2013

Published in JMLR Volume 15, Issue 1

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Cited By

Pradier MStojkoski VUtkovski ZKocorev LPerez-Cruz F(2018)Sparse Three-Parameter Restricted Indian Buffet Process for Understanding International Trade2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8462666(2476-2480)Online publication date: 15-Apr-2018
https://dl.acm.org/doi/10.1109/ICASSP.2018.8462666
Doshi-Velez FWilliamson S(2017)Restricted Indian buffet processesStatistics and Computing10.1007/s11222-016-9681-y27:5(1205-1223)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11222-016-9681-y
Vellanki PDuong TGupta SVenkatesh SPhung D(2017)Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic dataKnowledge and Information Systems10.1007/s10115-016-0971-751:1(127-157)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s10115-016-0971-7
Valera IRuiz FOlmos PBlanco CPerez-Cruz F(2016)Infinite continuous feature model for psychiatric comorbidity analysisNeural Computation10.1162/NECO_a_0080528:2(354-381)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1162/NECO_a_00805
Valera IGhahramani Z(2014)General table completion using a bayesian nonparametric modelProceedings of the 28th International Conference on Neural Information Processing Systems - Volume 110.5555/2968826.2968936(981-989)Online publication date: 8-Dec-2014
https://dl.acm.org/doi/10.5555/2968826.2968936

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