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

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2022
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2023-01-20
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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.
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Change-point detection, Latent variable models, Bayesian inference, Heterogeneous data, Psychiatric disorders
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