Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure

A Systematic Review and Meta-Analysis of Recent Randomized Controlled Trials Evaluating Effects of Psychosocial Interventions on Perinatal Depression

Depression is among the most common and burdensome health problems affecting pregnancy and the first-year postpartum (collectively, the perinatal period). Prior quantitative reviews have established both the overall efficacy of psychosocial interventions for perinatal depression and benefits of specific approaches. However, there are important knowledge gaps. We conducted a systematic review and meta-analysis of peer-reviewed articles published from 2021 and 2022 describing randomized controlled trials evaluating psychosocial interventions for perinatal depression. We aimed to evaluate the durability of intervention benefits, whether effects differ when interventions are embedded within medical settings, and whether effects differ across trials using mental health professionals vs. non-mental health professionals. Data from 2021-2022 articles yielded 63 studies representing 13,188 participants, and a total of 151 effect estimates. There was considerable uncertainty about durability of effects due to important methodological differences across trials and sparse long-term follow-up data. There was clear evidence of intervention benefits in studies utilizing non-mental-health providers, in both medical and non-medical settings. However, clear evidence of intervention benefits was not seen in trials utilizing mental health professionals as intervention providers. Findings highlighted the need to not only focus on overall estimates of benefits, but rather more thoroughly evaluate the data to understand the heterogeneity present

Combined supervised and unsupervised learning to identify subclasses of disease for better prediction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDisease subtyping, which aids in the development of personalised treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if I can identify subclasses of disease, this will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. In addition, patients might suffer from multiple disease complications. Models that are tailored to individuals could improve both prediction of multiple complications and understanding of underlying disease characteristics. However, AI models can become outdated over time due to either sudden changes in the underlying data, such as those caused by new measurement methods, or incremental changes, such as the ageing of the study population. This thesis proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The method was tested on a freely available dataset of real-world breast cancer cases and data from a London hospital on systemic sclerosis, a rare and potentially fatal condition. The results show that nearest consensus clustering classification improves accuracy and prediction significantly when this algorithm is compared with competitive similar methods. In addition, this thesis proposes a new algorithm that integrates latent class models with classification. The new algorithm uses latent class models to cluster patients within groups; this results in improved classification and aids in the understanding of the underlying differences of the discovered groups. The method was tested on data from patients with systemic sclerosis (SSc), a rare and potentially fatal condition, and coronary heart disease. Results show that the latent class multi-label classification (MLC) model improves accuracy when compared with competitive similar methods. Finally, this thesis implemented the updated concept drift method (DDM) to monitor AI models over time and detect drifts when they occur. The method was tested on data from patients with SSc and patients with coronavirus disease (COVID)

Prospective Associations of Homocysteine, Executive Function, and Depressive Symptoms

Associations of cardiovascular risk factors, cognitive performance, and depressive symptoms have been well established. However, the directionality of these associations as well as the specificity of these associations with respect to executive function are less clear. Additionally few studies have determined whether genetic risk factors, such as apolipoprotein-E4 (APOE-E4) genotype, and age moderate the associations of cardiovascular risk factors such as homocysteine with changes in depressive symptoms and how these associations may be mediated by cognitive performance. The primary aim of this study was to analyze the bidirectional associations of a full range of cognitive domains and symptoms of depression over a period of 5 years and to determine the extent to which the conditional associations of homocysteine (moderated by age and APOE-E4 genotype) and changes in depressive symptoms are mediated by cognitive performance. Additionally, we aimed to determine the extent to which these associations are specific to executive function as compared with other domains of cognitive function. After exclusions for probable dementia, kidney dialysis, and acute stroke, 719 adult participants were available for analysis for the sixth and seventh waves of the Maine-Syracuse Longitudinal Study. We conducted cross-sectional multiple linear regression analyses and cross-lagged panel analyses (CLPD) to determine the strength and directionality of associations for cognitive function and symptoms of depression. Next, we conducted conditional mediation path analyses to explore the associations of homocysteine (moderated by age and APOE-E4) and changes in self-reported depressive symptoms as mediated by cognitive function. All models were adjusted for wave 6 demographic covariates (age, sex, education, ethnicity, and marital status), cardiovascular risk profile (Framingham Risk Score), and depressive symptoms. In fully adjusted cross-sectional models, depressive symptoms were inversely associated with executive function and several other cognitive domains. In CLPD, cognitive performance was a stronger and more consistent predictor of changes in depressive symptoms (Executive Function, Global performance, Scanning and Tracking, and Visual-Spatial Organization and Memory) than depressive symptoms were of changes in performance. Although cognitive performance largely did not mediate the associations of cardiovascular risk factors (homocysteine and Framingham Risk Score) and changes in depressive symptoms, we did observe direct associations of Framingham Risk Score and changes in symptoms as well as significant moderation by age and APOE-E4 for the associations of homocysteine and changes in depressive symptoms. For APOE-E4 non-carriers, higher homocysteine was associated with symptom increases for individuals ≥ 74.33 years of age and for APOE-E4 carriers, there were marginal risks for individuals ≤ 45 years of age. The findings of this study have important clinical implications in assessing risk for and prevention of depressive symptoms both via maintenance of cognitive function and CVD risk reduction. Better executive functioning and performance in other cognitive domains was associated with lower levels of depressive symptoms over five years. Lower levels of CVD risk, both for the well-established CVD risk factors indexed by the Framingham Risk Score and for homocysteine, were associated with lower levels of depressive symptoms over five years. Moderation of depressive symptoms may be afforded through interventions designed to maintain executive function and to reduce risk relating to modifiable CVD risk factors such as homocysteine. Clinical trials with patient populations are needed to determine whether modification of homocysteine via dietary or physical activity adjustments could provide effective prevention of depressive symptoms

Probabilistic models for human behavior learning

The problem of human behavior learning is a popular interdisciplinary research topic that has been explored from multiple perspectives, with a principal branch of study in the context of computer vision systems and activity recognition. However, the statistical methods used in these frameworks typically assume short time scales, usually of minutes or even seconds. The emergence of mobile electronic devices, such as smartphones and wearables, has changed this paradigm as long as we are now able to massively collect digital records from users. This collection of smartphone-generated data, whose attributes are obtained in an unobtrusive manner from the devices via multiple sensors and apps, shape the behavioral footprint that is unique for everyone of us. At an individual level, the data projection also di ers from person to person, as not all sensors are equal, neither the apps installed, or the devices used in the real life. This point actually reflects that learning the human behavior from the digital signature of users is an arduous task, that requires to fuse irregular data. For instance, collections of samples that are corrupted, heterogeneous, outliers or have shortterm correlations. The statistical modelling of this sort of objects is one of the principal contributions of this thesis, that we study from the perspective of Gaussian processes (gp). In the particular case of humans, as well as many other life species in our world, we are inherently conditioned to the diurnal and nocturnal cycles that everyday shape our behavior, and hence, our data. We can study these cycles in our behavioral representation to see that there exists a perpetual circadian rhytm in everyone of us. This tempo is the 24h periodic component that shapes the baseline temporal structure of our behavior, not the particular patterns that change for every person. Looking to the trajectories and variabilities that our behavior may take in the data, we can appreciate that there is not a single repetitive behavior. Instead, there are typically several patterns or routines, sampled from our own dictionary, that we choose for every special situation. At the same time, these routines are arbitrary combinations of di erents timescales, correlations, levels of mobility, social interaction, sleep quality or will for working during the same hours on weekdays. Together, the properties of human behavior already indicate to us how we shall proceed to model its structure, not as unique functions, but as a dictionary of latent behavioral profiles. To discover them, we have considered latent variable models. The main application of the statistical methods developed for human behavior learning appears as we look to medicine. Having a personalized model that is accurately fitted to the behavioral patterns of some patient of interest, sudden changes in them could be early indicators of future relapses. From a technical point of view, the traditional question use to be if newer observations conform or not to the expected behavior indicated by the already fitted model. The problem can be analyzed from two perspectives that are interrelated, one more oriented to the characterization of that single object as outlier, typically named as anomaly detection, and another focused in refreshing the learning model if no longer fits to the new sequential data. This last problem, widely known as change-point detection (cpd) is another pillar of this thesis. These methods are oriented to mental health applications, and particularly to the passive detection of crisis events. The final goal is to provide an early detection methodology based on probabilistic modeling for early intervention, e.g. prevent suicide attempts, on psychiatric outpatients with severe a ective disorders of higher prevalence, such as depression or bipolar diseases.El problema de aprendizaje del comportamiento humano es un tema de investigación interdisciplinar que ha sido explorado desde múltiples perspectivas, con una línea de estudio principal en torno a los sistemas de visión por ordenador y el reconocimiento de actividades. Sin embargo, los métodos estadísticos usados en estos casos suelen asumir escalas de tiempo cortas, generalmente de minutos o incluso segundos. La aparición de tecnologías móviles, tales como teléfonos o relojes inteligentes, ha cambiado este paradigma, dado que ahora es posible recolectar ingentes colecciones de datos a partir de los usuarios. Este conjunto de datos generados a partir de nuestro teléfono, cuyos atributos se obtienen de manera no invasiva desde múltiples sensores y apps, conforman la huella de comportamiento que es única para cada uno de nosotros. A nivel individual, la proyección sobre los datos difiere de persona a persona, dado que no todos los sensores son iguales, ni las apps instaladas así como los dispositivos utilizados en la vida real. Esto precisamente refleja que el aprendizaje del comportamiento humano a partir de la huella digital de los usuarios es una ardua tarea, que requiere principalmente fusionar datos irregulares. Por ejemplo, colecciones de muestras corruptas, heterogéneas, con outliers o poseedoras de correlaciones cortas. El modelado estadístico de este tipo de objetos es una de las contribuciones principales de esta tesis, que estudiamos desde la perspectiva de los procesos Gaussianos (gp). En el caso particular de los humanos, así como para muchas otras especies en nuestro planeta, estamos inherentemente condicionados a los ciclos diurnos y nocturnos que cada día dan forma a nuestro comportamiento, y por tanto, a nuestros datos. Podemos estudiar estos ciclos en la representación del comportamiento que obtenemos y ver que realmente existe un ritmo circadiano perpetuo en cada uno de nosotros. Este tempo es en realidad la componente periódica de 24 horas que construye la base sobre la que se asienta nuestro comportamiento, no únicamente los patrones que cambian para cada persona. Mirando a las trayectorias y variabilidades que nuestro comportamiento puede plasmar en los datos, podemos apreciar que no existe un comportamiento único y repetitivo. En su lugar, hay varios patrones o rutinas, obtenidas de nuestro propio diccionario, que elegimos para cada situación especial. Al mismo tiempo, estas rutinas son combinaciones arbitrarias de diferentes escalas de tiempo, correlaciones, niveles de movilidad, interacción social, calidad del sueño o iniciativa para trabajar durante las mismas horas cada día laborable. Juntas, estas propiedades del comportamiento humano nos indican como debemos proceder a modelar su estructura, no como funciones únicas, sino como un diccionario de perfiles ocultos de comportamiento, Para descubrirlos, hemos considerado modelos de variables latentes. La aplicación principal de los modelos estadísticos desarrollados para el aprendizaje de comportamiento humano aparece en cuanto miramos a la medicina. Teniendo un modelo personalizado que está ajustado de una manera precisa a los patrones de comportamiento de un paciente, los cambios espontáneos en ellos pueden ser indicadores de futuras recaídas. Desde un punto de vista técnico, la pregunta clásica suele ser si nuevas observaciones encajan o no con lo indicado por el modelo. Este problema se puede enfocar desde dos perspectivas que están interrelacionadas, una más orientada a la caracterización de aquellos objetos como outliers, que usualmente se conoce como detección de anomalías, y otro enfocado en refrescar el modelo de aprendizaje si este deja de ajustarse debidamente a los nuevos datos secuenciales. Este último problema, ampliamente conocido como detección de puntos de cambio (cpd) es otro de los pilares de esta tesis. Estos métodos se han orientado a aplicaciones de salud mental, y particularmente, a la detección pasiva de eventos críticos. El objetivo final es proveer de una metodología de detección temprana basada en el modelado probabilístico para intervenciones rápidas. Por ejemplo, de cara a prever intentos de suicidio en pacientes fuera de hospitales con trastornos afectivos severos de gran prevalencia, como depresión o síndrome bipolar.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Pablo Martínez Olmos.- Secretario: Daniel Hernández Lobato.- Vocal: Javier González Hernánde

Negative Mood Reduces Self-Referential Memory Effects in an Online Object Ownership Simulation

The Self-Reference Effect (SRE) is a cognitive bias in which self-relevant stimuli are prioritised for processing. This bias allocates more attentional and encoding resources to selfrelevant objects making their memory traces more robust and easily retrievable. Research has repeatedly shown that self-owned and self-proximal objects benefit from this bias. However, little is known about the factors that impact the SRE. Emerging research suggests that emotion may attenuate the SRE. For instance, studies show that the salience of a self-related stimulus reduces when the stimulus is associated with negative self-referential information. However, there is limited research on how the SRE may be modulated by transient mood states. The major aim of the present study is to determine whether the SRE may be modulated by transient mood states. We investigated whether an induced negative mood state alters memory for self-related objects using an online emotion induction and shopping task. This task was selected because although SRE effects are robust in laboratory conditions, most studies rely on tasks with low external validity. All participants completed an online mood induction protocol (either negative or neutral mood induction). Thereafter, participants completed an online self-referencing object ownership task involving encoding (and subsequent recall) of self-owned, familiar other-owned, or unfamiliar other-owned everyday household shopping items. The group induced into a negative mood showed reduced memory recognition accuracy compared to the neutral mood group, with reduced memory for selfowned items. Further analyses revealed that negative mood interacted with both depression scores and object ownership to influence self-referential processing. Our results add to current SRE evidence and offer insights into how this bias can be influenced by both transient mood states and affective symptoms. Keywords: Self-reference, Object Ownership, Mood, Negative Emotion, Online