A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

Autor: Diego Castillo-Barnes, Ignacio Peis, Francisco J. Martínez-Murcia, Fermín Segovia, Ignacio A. Illán, Juan M. Górriz, Javier Ramírez, Diego Salas-Gonzalez

Publikováno v: Frontiers in Neuroinformatics, Vol 11 (2017)

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, inte

Externí odkaz: https://doaj.org/article/8422dffaacfd4b7db7c6df21922790f9

Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge

Autor: M. Mercedes Perez-Rodriguez, Enrique Baca-García, Marta Ruiz-Gomez, Javier D. López-Moríñigo, María Luisa Barrigón, Ignacio Peis, Antonio Artés-Rodríguez

Publikováno v: Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Biblos-e Archivo: Repositorio Institucional de la UAM
Universidad Autónoma de Madrid
Biblos-e Archivo. Repositorio Institucional de la UAM
instname

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Par

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ad93fd5641a54953eec0ac521624a95

Deep Sequential Models for Suicidal Ideation from Multiple Source Data

Autor: Philippe Courtet, Enrique Baca-García, María Luisa Barrigón, Constanza Vera-Varela, Pablo M. Olmos, Ignacio Peis, Antonio Artés-Rodríguez

Publikováno v: e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid
Universidad Carlos III de Madrid (UC3M)
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics, Institute of Electrical and Electronics Engineers, 2019, 23 (6), pp.2286--2293. ⟨10.1109/JBHI.2019.2919270⟩
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname

This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d5e58816689b730a64da85f71bd9a9d
http://arxiv.org/abs/1911.03522

MRI brain segmentation using hidden Markov random fields with alpha-stable distributions

Autor: Diego Salas-Gonzalez, Francisco Jesús Martínez-Murcia, Elmar Lang, Javier Ramírez, Juan Manuel Górriz, Ignacio Peis, Fermín Segovia, Ignacio A. Illán

Publikováno v: 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).

A MRI brain image segmentation method using a hidden Markov random fields with heavy-tailed alpha-stable distributions is presented. Each brain tissue is modelled using an alpha-stable distribution. Then, a HMRF is used to include spatial information

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ee39c8871b2457ea4c63dd1b3076aa50
https://doi.org/10.1109/nssmic.2016.8069422

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

Autor: Peis, Ignacio, Olmos, Pablo M., Vera-Varela, Constanza, Barrigon, Maria Luisa, Courtet, Philippe, Baca-Garcia, Enrique, Artes-Rodriguez, Antonio

Publikováno v: IEEE Journal of Biomedical & Health Informatics; Nov2019, Vol. 23 Issue 6, p2286-2293, 8p