Multi-source change-point detection over local observation models

In this work, we address the problem of change-point detection (CPD) on high-dimensional, multi-source, and heterogeneous sequential data with missing values. We present a new CPD methodology based on local latent variable models and adaptive factorizations that enhances the fusion of multi-source observations with different statistical data-type and face the problem of high dimensionality. Our motivation comes from behavioral change detection in healthcare measured by smartphone monitored data and Electronic Health Records. Due to the high dimension of the observations and the differences in the relevance of each source information, other works fail in obtaining reliable estimates of the change-points location. This leads to methods that are not sensitive enough when dealing with interspersed changes of different intensity within the same sequence or partial missing components. Through the definition of local observation models (LOMs), we transfer the local CP information to homogeneous latent spaces and propose several factorizations that weight the contribution of each source to the global CPD. With the presented methods we demonstrate a reduction in both the detection delay and the number of not-detected CPs, together with robustness against the presence of missing values on a synthetic dataset. We illustrate its application on real-world data from a smartphone-based monitored study and add explainability on the degree of each source contributing to the detection.This work has been partly supported by Spanish government (AEI/MCI) under grants RTI2018-099655-B-100, PID2021-123182OB-I00, PID2021-125159NB-I00, and TED2021-131823B-I00, by Comunidad de Madrid under grant IND2018/TIC-9649, IND2022/TIC- 23550, by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161, and by Comunidad de Madrid and FEDER through IntCARE-CM

Bifurcaciones en redes dinámicas: Aplicación al estudio de redes de neuronas

El funcionamiento del cerebro es un campo de investigación que actualmente se aborda desde muchas disciplinas. Entender cómo se transmite la información en una red de neuronas nos ayudará a entender cómo se transmite la información en el cerebro. Uno de estos enfoques viene dado desde las matemáticas, suponiendo la neurona como un sistema dinámico cuyo comportamiento viene determinado por un sistema de ecuaciones diferenciales ordinarias. Uno de los modelos más utilizado es el de Hodgkin-Huxley, que con tres ecuaciones modeliza la propagación de ondas eléctricas en la neurona tras la recepción de un estímulo (potenciales de acción). Sin embargo, estudiar el comportamiento de una red de neuronas y obtener resultados analíticos, teniendo en cuenta la complejidad de su dinámica, es una tarea realmente complicada. Una de las opciones es buscar otros sistemas cuya dinámica sea más sencilla pero con la suficiente riqueza como para tener la posibilidad de encontrar en su estudio comportamientos que pueden aparecer en dinámicas más complejas, y utilizarlos como posible guía. Éste es el objetivo de este trabajo: realizaremos un análisis del sistema replicador mutador desde la perspectiva de la Teoría de Bifurcaciones para dimensión tres, que veremos que puede interpretarse como una red de tres nodos, cada uno con una dinámica determinada por una de las tres ecuaciones que forman el sistema, y, posteriormente, comprobaremos mediante un estudio cualitativo del sistema que este tipo de comportamientos también van a aparecer en una red de tres neuronas, proporcionándonos en este caso información sobre los distintos patrones de sincronización que pueden darse en dicha red

Lectura de contexto y abordaje psicosocial desde los enfoques narrativos Sahagún, La Guajira.

El presente trabajo permite a cada uno de los estudiantes identificar mediante un relato de vida algunos aspectos psicosociales emergentes en un individuo reconociendo la violencia a la que ha sido sometida durante un periodo de su vida para luego mostrar la reinserción a la sociedad civil, asentando en práctica de esta manera cada uno de los saberes como psicólogos adquiridos durante todo un proceso de aprendizaje, además se logra apreciar como las narrativas posibilita a conectarse con la historia de otro identificando emociones que promueven una mejor intervención en una situación violenta. Elaborar las preguntas adecuadas facilita una intervención psicosocial apropiada pues se establecen conexiones que brindan respuestas para ofrecer posibles soluciones que ayuden a estas personas a pensar en un mejor mañana, porque los motiva a volver a soñar y tener un propósito para su vida. Los primeros auxilios psicológicos en el caso Pandurí especialmente pueden ayudar o exacerbar el dolor tanto emocional como físico de cada uno de sus pobladores víctimas de esta estigmatización y violencia, es por ello que se hace urgente hacer una intervención en crisis que prevenga secuelas emocionales futuras.The present work allows each of the students to identify through a life story some psychosocial aspects emerging in an individual recognizing the violence to which they have been subjected during a period of their life to then show the reintegration into civil society, settling in In this way, each one of the knowledge acquired as psychologists during a learning process is studied. In addition, it is possible to appreciate how narratives make it possible to connect with another's story by identifying emotions that promote a better intervention in a violent situation. Elaborating the right questions facilitates an appropriate psychosocial intervention because connections are established that provide answers to offer possible solutions that help these people to think about a better tomorrow, because it motivates them to dream again and have a purpose for their life. Psychological first aid in the Pandurí case can especially help or exacerbate the emotional and physical pain of each of its inhabitants who are victims of this stigmatization and violence, which is why it is urgent to make an intervention in crisis that prevents future emotional sequels

Activity-Oriented Antiedema Proprioceptive Therapy (TAPA) for Shoulder Mobility Improvement in Women with Upper Limb Lymphedema Secondary to Breast Cancer: A Multicenter Controlled Clinical Trial

Lymphedema, secondary to breast cancer (BCRL), is the abnormal accumulation of protein-rich fluid in the interstitium caused by a malfunction of the lymphatic system. It causes swelling, deficiencies in upper limb functions and structures, sensory pain and emotional alterations, which have a chronic course and affect the upper limb’s functionality. This study aims to verify the efficacy and efficiency in the upper limb´s functionality of a protocolized experimental approach based on occupational therapy, TAPA (activity-oriented proprioceptive antiedema therapy), in the rehabilitation of BCRL in stages I and II, comparing it with the conservative treatment considered as the standard, complex decongestive therapy (CDT), through a multicenter randomized clinical trial.The study has been financed in the call for competitive competition of research and innovation projects in the field of Primary Care, Regional Hospitals and High Resolution Hospital Centers of the Public Health System of Andalusia for the year 2021, of the Andalusian Public Foundation Progress and Health, according to the definitive list of funded projects, published on 23 December 2021, with EXP. No.: AP-0160-2021-C2-F2, also in the call for “Grants for research projects in Occupational Therapy. Call 2020”, granted by the Professional Association of Occupational Therapists of Extremadura (COPTOEX), Spain and, in 2020, received an economic contribution from the Research Group in Primary Health Care of Aragon (GAIAP-B21-17R group)

Impact of Activity-Oriented Propioceptive Antiedema Therapy on the Health-Related Quality of Life of Women with Upper-Limb Lymphedema Secondary to Breast Cancer—A Randomized Clinical Trial

Alterations derived from lymphedema in the upper-limb secondary to breast cancer-related lymphedema (BCRL) decrease the health-related quality of life (HRQoL), but there is limited evidence of the impact of the different interventions on it. The aim of this research was to compare the effect of conventional treatment with another treatment based on Activity-Oriented Antiedema Proprioceptive Therapy (TAPA) on HRQoL in women diagnosed with BCRL.This research was funded by the Call for research and innovation projects in the field of primary care, regional hospitals and high-resolution hospital centres of the Public Health System of Andalusia in 2021 by the Progreso y Salud Foundation, of the Ministry of Health and Families of the Junta de Andalucía, with EXP. No.: AP-0160-2021-C2-F2. He has also been awarded a scholarship by the Professional Association of Occupational Therapists of Extremadura (COPTOEX) (Spain) call 2020 and awarded a financial contribution from the Research Group in Primary Health Care of Aragon (GAIAP-B21-17R group), recognized and financed by the Government of Aragón (Spain) and by Feder Funds “Another way of making Europe”

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

I Congresso Ibero-Americano de Bibliotecas Escolares

Actas de la primera edición del I Congreso Iberoamericano de Bibliotecas Escolares, CIBES 2015, organizado por la Universidad Carlos III de Madrid (España), la Universidad Estatal Paulista (Brasil) y el Ayuntamiento de Getafe (España). Celebrado: 21 - 23 de octubre de 2015 en la Universidad Estatal Paulista (Marília) y 26 - 28 de octubre de 2015 en la Universidad Carlos III de Madrid (Getafe)Universidad Carlos III de Madrid (España)Universidad Estatal Paulista (Brasil)Ayuntamiento de Getafe (España)Dimensiones y visiones de la biblioteca escolar en una Educación por competencias: la necesidad de una política estratégica / Miguel Ángel Marzal. -- Getafe ciudad educadora, lectora y escritora: Bibliotecas escolares / Lourdes Muñoz Santiuste. -- Presente y futuro: biblioteca escolar-CREA y proyectos interdisciplinares / Rosa Piquín. -- Cultura en información: un reto esencial de la biblioteca escolar / Mónica Baró. -- Bibliotecas escolares de Galicia: un mundo de oportunidades a favor de la Educación / Cristina Novoa. -- 10 años de la Red de Bibliotecas Escolares de Extremadura (REBEX) / Casildo Macías Pereira. -- Biblioteca Escolar y uso ético de la información para una Cultura de Paz / Ana Barrero Tíscar. -- Dinamización de la Biblioteca Escolar Plumita durante el curso escolar 2014/15 / María Antonia Cano Cañada. -- Experiencia de la creación de una biblioteca escolar / Susana Santos Martín. -- Grupo cooperativo Bibliotecas escolares en Red-Albacete / José Manuel Garrido Argandoña y Eva Leal Scasso. -- La BCREA "Juan Leiva". El fomento de la lectura desde la web social / Andrés Pulido Villar. -- Proceso de implantación de una herramienta de autoevaluación en la red de bibliotecas escolares de Extremadura (REBEX) / Casildo Macías Pereira. -- La biblioteca escolar: abriendo fronteras / Lorena Verónica Cabrera Orellana. -- O programa RBE e a avaliaçao das bibliotecas escolares: melhoria, desenvolvimiento e innovaçao / Elsa Conde. -- Profesional de Biblioteconomía y Documentación: esencial en la plantilla de la escuela / Pilar del Campo Puerta. -- Una mirada activa al proceso educativo desde la biblioteca escolar / María Jesús Fontela Fernández . -- Con otra mirada "La ilustración como vehículo de comunicación y aprendizaje en las bibliotecas escolares" / Pablo Jurado Sánchez-Galán. -- Fingertips. Recriar a biblioteca escolar na sala de aula / Rui Alfonso Mateus. -- Hablemos de libros. Cómo transformar una clase de literatura en una comunidad de interpretación de textos / Francisco César Díaz Rey. -- Inclusión social de familias inmigrantes a través de un programa de aprendizaje de la lengua castellana / Ana Carmen Tolino Fernández-Henarejos. -- O desenvolvimento de atividades de mediação de leitura em biblioteca escolar: o caso da biblioteca da Escola Sesc de Ensino Médio / Vagner Amaro. -- La biblioteca escolar. Proceso de enseñanza-aprendizaje de padres a hijos / Ana Carmen Tolino Fernández- Henarejos. -- Leo con y para los demás / Ismael Fernández Fernández, Ana María Moreno Vicente y Ana Beatriz Vicente Pérez. -- Nanas y arrullo. Poesía a la deriva / Bernardo Fuentes Navarrete y Carlos García-Romeral Pérez. -- Gestión y evaluación de servicios bibliotecarios para personas con dislexia: una biblioteca escolar inclusiva desde una perspectiva internacional / Carmen Jorge García-Reyes. -- Sueños lectores compartidos hechos realidad: la biblioteca escolar del C.E.I.P-S.E.S-A.A “LA PAZ” de Albacete / Ana Rosa Cabañero Tobarra, Juan Manuel Herráez, Eva Leal Scasso, María Marín Sánchez, Ana Belén Medrano Martínez y María José Nortes Ruipérez. -- El programa biblioteca escuela en Civican. La literatura como elemento motivador para la alfabetización informacional / Villar Arellano Yanguas. -- La competencia digital en el diseño curricular: desde la biblioteca al aula / Felicidad Campal García. -- O deselvomimento da pesquisa escolar por meio da competência em informaçao / Luciane de Fátima Cavalcante Beckman y Marta Leandro da Mata. -- Proyecto escolar de investigación documental "Te pillé leyendo" / José Manuel Garrido Argandoña. -- Aprender com a Biblioteca Escolar: formar para as literacias / Paula Correia y Isabel Mendinhos. -- Sucedió en el siglo XX / María Antonia Becerra Montalbán, Ángel Bernabé Muñoz y Sofía Vaz Romero. -- El Club de lectura en la nube / Belén Benito Blázquez y Ana Ordás García. -- Promover a leitura e a escrita na era digital: prácticas nas bibliotecas escolares / María Raquel Ramos. -- A biblioteca escolar e o desafío da interculturalidade: o projeto Ser + cidadao / María da Conceição Tomé. -- Cuando la competencia digital encontró a la alfabetización informacional o Mucho ruido y pocas nueces / Felicidad Campal García. -- Hora de ler, un programa para el fomento de la lectura en contexto educativo / Cristina Novoa. -- Hábitos de lectura para las competencias en información y alfabetización en información en bibliotecas escolares de Puerto Rico / Karen Denise Centeno Casillas. -- Repositorios digitales en las bibliotecas escolares andaluzas: situación, modelos y herramientas para su creación / Dolores Olmos Olmos y Andrés Pulido Villar. -- Trabajando las competencias clave con las aventuras de Mozarito en Extremadura / María Teresa Carballosa González y María Esther Nieto Vidal. -- Análisis de modelos de evaluación de la web de la biblioteca escolar / Raúl Cremades García. -- Emociónate con las historias: El bosque de las emociones e historias con mucho teatro / Esther Luis Pérez y Ana María Peromingo Fernández. -- Biblioteca escolar de innovación y continuación / E. María Guerrero Palacios y Silvia Mora Ramírez. -- Uso de estándares y licencias para la creación y difusión de contenidos en las bibliotecas escolares / José Luis Barreiro Cebey. -- La biblioteca escolar digital móvil / Javier Fernández Delgado. -- Uso de aplicaciones móviles para el desarrollo de la competencia lingüística. Proyecto Hansel App Gretel / Dolores Olmos Olmos. -- A memória e a mediação segundo Vigotski / Leda Maria Araújo, Patricia Celia Santana, Sueli Bortolin y Leticia Gorri Molina. -- Bibliotecas escolares como tema de estudo dos alunos de graduação em blioteconomia do Instituto de Ensino Superior da FUNLEC: estado da arte / Tiago Pereira Nocera y Rodrigo Pereira. -- Ações de mediação da leitura e da informação em bibliotecas escolares: um olhar sobre as bibliotecas dos Colégios de Aplicação / Tatyanne Christina Gonçalves Ferreira Valdez y Alberto Calil Júnior. -- Mediação pedagógica numa biblioteca de escola pública em Londrina / Rovilson José da Silva, Teba Silva Yllana y Sueli Bortolin. -- Utilização de categorias por cores em sistema de biblioteca voltado ao público infanto-juvenil / Liliana Giusti Serra. -- Atividades de ensino dos atos de leitura com crianças em risco social / Adriana Naomi Fukushima da Silva y Dagoberto Buim Arena. -- Biblioteca escolar: espaço de significados entre alunos, professores e bibliotecários / Rodrigo Barbosa Paulo, Marisa Xavier, Helen Castro Casarin y Creuza Barbaroto. -- A Biblioteca Escolar no Contexto da Legislação e do Processo Educativo / Eliane Lourdes da Silva Moro, Francisca Rosaline Leite Mota y Raimundo Martins de Lima. -- O jornal impresso como fonte de informação: a importância da formação de leitores críticos / Mariana Pícaro Cerigatto. -- Bibliotecas escolares no estado do Rio Grande do Sul: a trajetória de realização dos fóruns gaúchos pela melhoria das bibliotecas escolares / Eliane Lourdes da Silva Moro y Lizandra Brasil Estabel. -- O acesso à informação dos usuários surdos na biblioteca escolar / André Luís Onório Coneglian y Mayara Melo Santana. -- Aprendizagem coletiva de bibliotecários e a competência de pesquisa dos docentes: o caso do Instituto Federal do Espírito Santo / Maristela Almeida Mercandeli Rodrigues y Beatriz Quiroz Villardi. -- Biblioteca escolar: atores, parâmetros e competências / Mavi Galante Mancera Dall´Acqua Carvalho y Claudio Marcondes de Castro Filho. -- Estratégias de aprendizagem de escrita no Ensino Fundamental II / Érika Christina Kohle. -- Bebês e livros: leitura nas bebetecas. Kenia Adriana de Aquino Modesto Silva, Juliane Francischeti Martins Motoyama y Renata Junqueira de Souza. -- Práticas alternativas para organização de acervos nos espaços de leitura em ambientes escolares / Luciana Souza Gracioso, Ariovaldo Alves, Débora Nascimento, Suelen Redondo, Tainara Torika Kiri de Castro, Elizabete Angelon y Eduardo Barbosa. -- Reflexões sobre a modelagem e criação de uma Rede Virtual de Leitores para Bibliotecas Escolares / Carla Floriana Martins y Raoni Guerra Rajão. -- Biblioteca escolar: espaço de formação leitora? / Silvana Ferreira de Souza Balsan y Renata Junqueira de Souza. -- “Se a Biblioteca Escolar é minha mãe, o Google é meu pai”: representações da relação entre Biblioteca Escolar e Google no imaginário de alunos do ensino técnico / Adriana Bogliolo Sirihal-Duarte, Maria L. Amorim Antunes y Raquel Miranda Vilela Paiva. -- Desafios e propostas para a universalização das bibliotecas escolares no Brasil e na Espanha / Rodrigo Pereira, Daniela Spudeit y Fernanda de Sales. -- Bibliotecário educador: possibilidades de atuação no contexto da biblioteca escolar / André Carlos da Silva, Valéria Martin Valls y Mariana de Paula Silva. -- Uma ONG para Bibliotecas Escolares : estratégia para ampliar a igualdade e capacidade de acesso e uso da informação e educação escolar de qualidade / Suelen Camilo Ferreira y Luciana de Souza Gracioso. -- O aluno com deficência: o papel do bibliotecário na disponibilidade de recursos acessíveis na biblioteca escolar / Adriano de Sales Coelho, Rosilene de Melo Oliveira y Marcos Pastana Santos. -- Biblioteca digital virtual e o uso do tablete: uma possibilidade de construção de novas práticas de leitura na escola / Barbara Cibelli da Silva Monteagudo y Dagoberto Buim Arena. -- A importância da biblioteca na educação de crianças de 0 a 3 anos / Yngrid Karolline Mendonça Costa y Cyntia Graziella Guizelim Simões Girotto. -- Comportamento Informacional de adolescentes: a relação com bibliotecas e escolas / Nelson Sebastian Silva-Jerez y Helen de Castro S. Casarin

Change-Point Detection Methods for Behavioral Shift Recognition in Mental Healthcare

Human behavior analysis has been approached from different perspectives along time. In recent years, the emergence of new technologies and digitalization advances have risen as an alternative tool for behavior characterization, as well as for the detection of changes over time. In particular, the generalized use of smartphones and electronic devices, which are continuously collecting data from the user, provide a representation of behavior in different areas of a person’s life, such as mobility, physical activity or social interactions. In addition, they allow us a passive monitorization, that is, without the need for the user to interact directly with the device, collecting information in a unobtrusive manner and therefore without altering their daily routine. This methodology implies, among other advantages, that the user does not subjectively influence the information collected, obtaining objective representations of their behavior. This approach to the characterization and analysis of behavior and its changes has many applications, notably in medicine. In this work, we focus specifically on the field of mental health, where the characterization and early detection of behavioral changes is important in order to prevent relapses in psychiatric patients and, in particular, in those with a history of suicidal behavior to try to prevent possible suicide attempts or psychiatric emergency admissions. Our approach is based on the development and application of mathematical and statistical models that can help us to detect these changes from passively collected data. However, despite the mentioned advantages, working with data collected through electronic devices and, specifically in a clinical scenario, is a challenge due to its characteristics. These are data with a very complex structure since, first of all, they are irregularly sampled in time (the samples can be stored every 5 minutes, when a specific activity starts or daily). Second, each observation can be heterogeneous, where by heterogeneous we mean that it is made up of several sources of different statistical type (continuous, discrete) or same type but, statistically, with different marginal distributions. In addition, the existence of several sources and the frequency of the samples causes that each day is represented by a high-dimensional vector, focusing on the need for scalable algorithms. Lastly, these are data sequences with many missing values and very diverse patterns due, for example, to the lack of permissions on the phone, disconnection periods or, simply, the temporal irregularity already mentioned. The preprocessing of data with these characteristics requires a huge effort and time cost that is not feasible when dealing with such a demanding goal, as it is the prediction and prevention of suicide attempts, since the information must be processed in real time every minute is important. Therefore, we need methods that are fast, efficient, accurate and adapted to the complexity of the data we are working with. For this reason, instead of focusing our efforts on data mining, which is generally conditioned to a specific initial hypothesis and hinders reproducibility, we work on methods that are capable of handling data sequences with the previously aforementioned characteristics, and do it in an online manner. That is, algorithms capable of processing the samples as they are being recorded. In this thesis, we focus on the development of probabilistic models for behavior change detection, proposing algorithms that can work on heterogeneous, multi-source, high-dimensional sequential data with missing values. In our scenario, we assume that the joint distribution of the data changes at a given moment, segmenting the sequence, and our goal is to detect this change and to do so with the least possible delay. We begin by describing the benefits of using digital phenotyping for the characterization of human behavior changes, and we introduce an example of a specific monitoring e-health system with which we have worked. We present two works on data mining in medicine through digital phenotype modelling: the prediction of disability level in different domains of daily life and the analysis of causal relationships between variables in order to detect negative effects caused by isolation during the Covid-19 pandemic in psychiatric patients. In the following -more technical- chapters, we go a step further, and change the focus: from fully adapting our data to existing methods, to proposing algorithms that are specific for heterogeneous, multi-source, high-dimensional sequential data with missing values. We focus on the development of change point detection (CPD) algorithms and present the benefits of using latent variable models to deal with the problem of high-dimensional data sets, and provide methods that are able of integrating data from different statistical type. We also present a flexible CPD model that works on local observation models (LOMs) defined based on the statistical type, source or previous knowledge of the initial data, generated from local discrete latent variable models. In this way, the information is transformed into homogeneous low-dimensional spaces, maintaining the benefits of the previously proposed algorithms but also allowing an equivalent level of treatment of all local representations, thus solving the initial problem of heterogeneity. In addition, different CPD factorization models are defined and adapted that weight the contribution of each local representation to the global detection following different approaches, holding for every previously proposed local observation models, and adding explainability on the degree of contribution of each local representation to the joint detection. We evaluated and tested the proposed models on synthetic data, demonstrating an improvement in the precision and a reduction in the delay of the detection, proving their robustness against the presence of missing data. Finally, we apply some of these methods to a real data set within a study of behavioral change characterization in psychiatric patients with a history of suicide-related events. We present individualized models for change detection over The preprocessing of data with these characteristics requires a huge effort and time cost that is not feasible when dealing with such a demanding goal, as it is the prediction and prevention of suicide attempts, since the information must be processed in real time every minute is important. Therefore, we need methods that are fast, efficient, accurate and adapted to the complexity of the data we are working with. For this reason, instead of focusing our efforts on data mining, which is generally conditioned to a specific initial hypothesis and hinders reproducibility, we work on methods that are capable of handling data sequences with the previously aforementioned characteristics, and do it in an online manner. That is, algorithms capable of processing the samples as they are being recorded. In this thesis, we focus on the development of probabilistic models for behavior change detection, proposing algorithms that can work on heterogeneous, multi-source, high-dimensional sequential data with missing values. In our scenario, we assume that the joint distribution of the data changes at a given moment, segmenting the sequence, and our goal is to detect this change and to do so with the least possible delay. We begin by describing the benefits of using digital phenotyping for the characterization of human behavior changes, and we introduce an example of a specific monitoring e-health system with which we have worked. We present two works on data mining in medicine through digital phenotype modelling: the prediction of disability level in different domains of daily life and the analysis of causal relationships between variables in order to detect negative effects caused by isolation during the Covid-19 pandemic in psychiatric patients. In the following -more technical- chapters, we go a step further, and change the focus: from fully adapting our data to existing methods, to proposing algorithms that are specific for heterogeneous, multi-source, high-dimensional sequential data with missing values. We focus on the development of change point detection (CPD) algorithms and present the benefits of using latent variable models to deal with the problem of high-dimensional data sets, and provide methods that are able of integrating data from different statistical type. We also present a flexible CPD model that works on local observation models (LOMs) defined based on the statistical type, source or previous knowledge of the initial data, generated from local discrete latent variable models. In this way, the information is transformed into homogeneous low-dimensional spaces, maintaining the benefits of the previously proposed algorithms but also allowing an equivalent level of treatment of all local representations, thus solving the initial problem of heterogeneity. In addition, different CPD factorization models are defined and adapted that weight the contribution of each local representation to the global detection following different approaches, holding for every previously proposed local observation models, and adding explainability on the degree of contribution of each local representation to the joint detection. We evaluated and tested the proposed models on synthetic data, demonstrating an improvement in the precision and a reduction in the delay of the detection, proving their robustness against the presence of missing data. Finally, we apply some of these methods to a real data set within a study of behavioral change characterization in psychiatric patients with a history of suicide-related events. We present individualized models for change detection over The preprocessing of data with these characteristics requires a huge effort and time cost that is not feasible when dealing with such a demanding goal, as it is the prediction and prevention of suicide attempts, since the information must be processed in real time every minute is important. Therefore, we need methods that are fast, efficient, accurate and adapted to the complexity of the data we are working with. For this reason, instead of focusing our efforts on data mining, which is generally conditioned to a specific initial hypothesis and hinders reproducibility, we work on methods that are capable of handling data sequences with the previously aforementioned characteristics, and do it in an online manner. That is, algorithms capable of processing the samples as they are being recorded. In this thesis, we focus on the development of probabilistic models for behavior change detection, proposing algorithms that can work on heterogeneous, multi-source, high-dimensional sequential data with missing values. In our scenario, we assume that the joint distribution of the data changes at a given moment, segmenting the sequence, and our goal is to detect this change and to do so with the least possible delay. We begin by describing the benefits of using digital phenotyping for the characterization of human behavior changes, and we introduce an example of a specific monitoring e-health system with which we have worked. We present two works on data mining in medicine through digital phenotype modelling: the prediction of disability level in different domains of daily life and the analysis of causal relationships between variables in order to detect negative effects caused by isolation during the Covid-19 pandemic in psychiatric patients. In the following -more technical- chapters, we go a step further, and change the focus: from fully adapting our data to existing methods, to proposing algorithms that are specific for heterogeneous, multi-source, high-dimensional sequential data with missing values. We focus on the development of change point detection (CPD) algorithms and present the benefits of using latent variable models to deal with the problem of high-dimensional data sets, and provide methods that are able of integrating data from different statistical type. We also present a flexible CPD model that works on local observation models (LOMs) defined based on the statistical type, source or previous knowledge of the initial data, generated from local discrete latent variable models. In this way, the information is transformed into homogeneous low-dimensional spaces, maintaining the benefits of the previously proposed algorithms but also allowing an equivalent level of treatment of all local representations, thus solving the initial problem of heterogeneity. In addition, different CPD factorization models are defined and adapted that weight the contribution of each local representation to the global detection following different approaches, holding for every previously proposed local observation models, and adding explainability on the degree of contribution of each local representation to the joint detection. We evaluated and tested the proposed models on synthetic data, demonstrating an improvement in the precision and a reduction in the delay of the detection, proving their robustness against the presence of missing data. Finally, we apply some of these methods to a real data set within a study of behavioral change characterization in psychiatric patients with a history of suicide-related events. We present individualized models for change detection over passively-sensed data via smartphones, and use suicide attempts and psychiatric emergency admissions as real labels with the aim of predicting them one week in advance.El análisis del comportamiento humano se ha abordado a lo largo del tiempo desde distintas perspectivas. En los últimos años, el auge de las nuevas tecnologías y los avances en digitalización se han presentado como una herramienta alternativa para la caracterización de éste, así como para la detección de cambios a lo largo del tiempo. En particular, el uso extendido de smartphones y dispositivos electrónicos, que recogen datos de manera continua del usuario, proporcionan una representación diaria del comportamiento en distintos ámbitos de la vida de una persona como son la movilidad, la actividad física o las interacciones sociales. Además, permiten la monitorización pasiva, es decir, sin necesidad de que el usuario interactúe directamente con el dispositivo, recogiendo información de manera no intrusiva y sin alterar por tanto su rutina diaria. Esta metodología supone, entre otras ventajas, que el usuario no influya subjetivamente en la información recogida, obteniendo representaciones objetivas de su comportamiento. Esta aproximación para la caracterización y análisis de comportamiento y cambios en el mismo tiene muchas aplicaciones, notablemente en medicina. En este trabajo nos centramos en concreto en el campo de la salud mental, donde la caracterización y detección temprana de cambios de comportamiento es importante de cara a prevenir recaídas en pacientes psiquiátricos y, en particular, en aquellos con antecedentes de comportamientos suicidas para intentar prevenir posibles intentos de suicidio o ingresos en urgencias psiquiátricas. Nuestro enfoque se basa en el desarrollo y aplicación de modelos matemáticos y estadísticos que puedan ayudarnos a detectar estos cambios a partir de datos tomados de manera pasiva. Sin embargo, a pesar de las ventajas mencionadas, trabajar con datos recogidos a través de dispositivos electrónicos y, específicamente en el ámbito clínico, supone un reto debido a sus características. Se trata de datos con estructura muy compleja ya que, en primero lugar, son irregulares en tiempo (las muestras pueden guardarse cada 5 minutos, cuando se desarrolla una actividad concreta o cada día). En segundo lugar, cada observación puede ser heterogénea, donde con heterogénea nos referimos a que se compone de varias fuentes de distinto tipo estadístico (continuo, discreto) o del mismo tipo pero, estadísticamente, con distintas distribuciones marginales. Además, la existencia de varias fuentes y la frecuencia de las muestras, hace que cada día esté representado por un vector que puede ser de una dimensión muy alta, poniendo el foco en la necesidad de algoritmos escalables. Por último, se trata de secuencias de datos con muchos valores perdidos y con patrones muy diversos debido, por ejemplo, a la falta de permisos en el teléfono, intervalos de desconexión o, simplemente, la irregularidad temporal ya comentada. El preprocesado de datos con estas características requiere de un enorme esfuerzo y cantidad de tiempo que no es viable cuando lidiamos con un objetivo tan exigente como es la predicción y prevención de intentos de suicidio, ya que la información debe ser tratada a tiempo real y cada minuto cuenta. Por tanto, necesitamos métodos que sean rápidos, eficientes, precisos y adaptados a la complejidad de los datos con los que trabajamos. Por eso, en vez de centrar nuestro esfuerzo en la explotación de datos, que generalmente está condicionada a una hipótesis inicial concreta y dificulta la reproducibilidad, trabajamos en métodos que sean capaces de manejar las secuencias de datos con las características que se han comentado previamente, y hacerlo de manera online. Es decir, algoritmos capaces de procesar las muestras a medida que van siendo registradas. En esta tesis, nos centramos en el desarrollo de modelos probabilísticos de detección de cambios de comportamiento, proponiendo algoritmos que puedan trabajar sobre datos secuenciales heterogéneos, de múltiples fuentes y de alta dimensión con valores perdidos. En nuestro escenario, asumimos que la distribución conjunta de los datos cambia en un momento dado, segmentando la secuencia, y siendo nuestro objetivo detectar ese cambio y hacerlo con el menor retraso temporal posible. Comenzamos describiendo los beneficios del uso de fenotipo digital para la caracterización del cambio de comportamiento humano, e introducimos un ejemplo de sistema e-health de monitorización concreto con el que se ha trabajado. Presentamos dos trabajos de explotación de datos en medicina a través de modelado de fenotipo digital: la predicción de funcionalidad en los distintos dominios de la vida diaria y el análisis de relaciones causales entre variables de cara a detectar efectos negativos causados por el aislamiento durante la pandemia del Covid-19 en pacientes psiquiátricos. En los siguientes capítulos, de corte más técnico, vamos un paso más allá, y cambiamos el foco: de adaptar nuestros datos totalmente a los métodos existentes, a proponer algoritmos que sean específicos para datos secuenciales heterogéneos, de múltiples fuentes y de alta dimensión con valores perdidos. Nos centramos en el desarrollo de algoritmos de detección de puntos de cambio (CPD) y presentamos los beneficios de utilizar modelos generativos de variable latente para lidiar con el problema de data sets de alta dimensionalidad y proporcionar métodos capaces de integrar datos de distinto tipo estadístico. Presentamos también un modelo de CPD flexible que trabaja sobre modelos de observación locales (LOMs) definidos en base al tipo estadístico, fuente o conocimiento previo de los datos iniciales, generados a partir de modelos discretos de variable latente locales. De esta forma, la información es transformada a espacios homogéneos de baja dimensionalidad, manteniendo los beneficios de los algoritmos previamente propuestos pero permitiendo además un tratamiento equivalente de todos las representaciones locales, solucionando así el problema inicial de heterogeneidad. Además, se definen y adaptan distintos modelos de factorización de CPD que ponderan la contribución de cada representación local al la detección global de distinta manera, siendo válidos para cualquiera de los modelos de observación local previamente propuestos, y agregando explicabilidad sobre el grado de contribución de cada representación local a la detección conjunta. Evaluamos y probamos los modelos propuestos en datos sintéticos, demostrando una mejora en la precisión y la reducción en el retraso de detección de puntos de cambio, mostrando ser robustos ante la presencia de datos perdidos. Finalmente, aplicamos algunos de estos métodos a datos reales en un estudio de caracterización de cambios de comportamiento en pacientes psiquiátricos con antecedentes suicidas. Presentamos modelos individualizados de detección de cambio sobre datos recogidos de manera pasiva a través del smartphone y usamos los intentos de suicidio e ingresos en urgencias psiquiátricas como etiquetas reales con el objetivo de predecirlos con una semana de antelación.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Fernando Pérez Cruz.- Secretario: Jorge López Castromán.- Vocal: Vanessa Gómez Verdej

Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection

Bayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavioral study.This work was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grants TEC2017-86921-C2-2-R (CAIMAN) and RTI2018-099655-B-I00 (CLARA), and by the Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM). The work of PMM has been supported by FPI grant BES-2016-077626 and ERC funding under the EU’s Horizon 2020 research and innovation programme (grant agreement nº 757360). LRM has been supported by grant IND2018/TIC-9649 from the Comunidad de Madrid