2,300 research outputs found
Quantifying Quality of Life
Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject
Quantifying Quality of Life
Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject
Predicting emotional states using behavioral markers derived from passively sensed data: Data-driven machine learning approach
Background:
Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues.
Objective:
This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations.
Methods:
Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient.
Results:
Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data.
Conclusions:
These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement number 813533. This work was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. The authors thank Enrique Baca-Garcia for providing demographic and clinical data and assisting in interpreting and summarizing the data
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Data’s Intimacy: Machinic Sensibility and the Quantified Self
Today, machines observe, record, sense the world – not just for us, but sometimes instead of us (in our stead), and even indifferently to us humans. And yet, we remain human. Correlationism may not be up to a comprehensive ontology, but the ways in which we encounter, and struggle to make some kind of sense of, machinic sensibility matters. The nature of that encounter is not instrumentality, or even McLuhanian extension, but a full-blown ‘relationship’ where the terms by which machines ‘experience’ the world, and communicate with each other, parametrises the conditions for our own experience. This essay will play out one such relationship currently in the making: the boom in self-tracking technologies, and the attendant promise of data’s intimacy.
This essay proceeds in three sections, all of which draw on a larger research project into self-tracking and contemporary data epistemologies. It thus leverages observations from close reading of self-tracking’s publicisation in the mass media between 2007 and 2016; analysis of over fifty self-tracking products, some of it through self-experimentation; and interviews and ethnographic observation, primarily of the ‘Quantified Self’ connoisseur community. The first section examines the dominant public presentations of self-tracking in early twenty-first century discourse. This discourse embraces a vision of automated and intimate self-surveillance, which is then promised to deliver superior control and objective knowledge over the self. Next, I link these promises to the recent theoretical turns towards the agency of objects and the autonomous sensory capacities of new media to consider the implications of such theories – and the technological shifts they address – for the phenomenology of the new media subject. Finally, I return to self-tracking discourse to consider its own idealisation of such a subject – what I call ‘data-sense’. I conclude by calling for a more explicit public and intellectual debate around the relationships we forge with new technologies, and the consequences they have for who – and what – is given which kinds of authority to speak the truth of the ‘self’
A step towards Advancing Digital Phenotyping In Mental Healthcare
Smartphones and wrist-wearable devices have infiltrated our lives in recent years. According
to published statistics, nearly 84% of the world’s population owns a smartphone,
and almost 10% own a wearable device today (2022). These devices continuously generate
various data sources from multiple sensors and apps, creating our digital phenotypes.
This opens new research opportunities, particularly in mental health care, which has previously
relied almost exclusively on self-reports of mental health symptoms.
Unobtrusive monitoring using patients’ devices may result in clinically valuable markers
that can improve diagnostic processes, tailor treatment choices, provide continuous
insights into their condition for actionable outcomes, such as early signs of relapse, and
develop new intervention models. However, these data sources must be translated into
meaningful, actionable features related to mental health to achieve their full potential.
In the mental health field, there is a great need and much to be gained from defining a
way to continuously assess the evolution of patients’ mental states, ideally in their everyday
environment, to support the monitoring and treatments by health care providers. A
smartphone-based approach may be valuable in gathering long-term objective data, aside
from the usually used self-ratings, to predict clinical state changes and investigate causal
inferences about state changes in patients (e.g., those with affective disorders).
Being objective does not imply that passive data collection is also perfect. It has several
challenges: some sensors generate vast volumes of data, and others cause significant
battery drain. Furthermore, the analysis of raw passive data is complicated, and collecting
certain types of data may interfere with the phenotype of interest. Nonetheless, machine
learning is predisposed to address these matters and advance psychiatry’s era of personalised
medicine.
This work aimed to advance the research efforts on mobile and wearable sensors for
mental health monitoring. We applied supervised and unsupervised machine learning
methods to model and understand mental disease evolution based on the digital phenotype
of patients and clinician assessments at the follow-up visits, which provide ground
truths. We needed to cope with regularly and irregularly sampled, high-dimensional, and
heterogeneous time series data susceptible to distortion and missingness. Hence, the developed
methods must be robust to these limitations and handle missing data properly.
Throughout the various projects presented here, we used probabilistic latent variable
models for data imputation and feature extraction, namely, mixture models (MM) and hidden
Markov models (HMM). These unsupervised models can learn even in the presence
of missing data by marginalising the missing values in the function of the present observations. Once the generative models are trained on the data set with missing values, they can
be used to generate samples for imputation. First, the most probable component/state has
to be found for each sample. Then, sampling from the most probable distribution yields
valid and robust parameter estimates and explicit imputed values for variables that can
be analysed as outcomes or predictors. The imputation process can be repeated several
times, creating multiple datasets, thereby accounting for the uncertainty in the imputed
values and implicitly augmenting the data. Moreover, they are robust to moderate deviations
of the observed data from the assumed underlying distribution and provide accurate
estimates even when missingness is high.
Depending on the properties of the data at hand, we employed feature extraction
methods combined with classical machine learning algorithms or deep learning-based
techniques for temporal modelling to predict various mental health outcomes - emotional
state, World Health Organisation Disability Assessment Schedule (WHODAS 2.0) functionality
scores and Generalised Anxiety Disorder-7 (GAD-7) scores, of psychiatric outpatients.
We mainly focused on one-size-fits-all models, as the labelled sample size per
patient was limited; however, in the mood prediction case, it was possible to apply personalised
models.
Integrating machines and algorithms into the clinical workflow require interpretability
to increase acceptance. Therefore, we also analysed feature importance by computing
Shapley additive explanations (SHAP) values. SHAP values provide an overview of essential
features in the machine learning models by designating the weight of predictability
of each feature positively or negatively to the target variable.
The provided solutions, as such, are proof of concept, which require further clinical
validation to be deployable in the clinical workflow. Still, the results are promising
and lay some foundations for future research and collaboration among clinicians, patients,
and computer scientists. They set the paths to advance future research prospects in
technology-based mental healthcare.En los últimos años, los smartphones y los dispositivos y pulseras inteligentes, comúnmente
conocidos como wearables, se han infiltrado en nuestras vidas. Según las estadÃsticas
publicadas a dÃa de hoy (2022), cerca del 84% de la población tiene un smartphone y
aproximadamente un 10% también posee un wearable. Estos dispositivos generan datos
de forma continua en base a distintos sensores y aplicaciones, creando asà nuestro fenotipo
digital. Estos datos abren nuevas vÃas de investigación, particularmente en el área de salud
mental, dónde las fuentes de datos han sido casi exclusivamente autoevaluaciones de sÃntomas
de salud mental.
Monitorizar de forma no intrusiva a los pacientes mediante sus dispositivos puede dar
lugar a marcadores valiosos en aplicación clÃnica. Esto permite mejorar los procesos de
diagnóstico, adaptar tratamientos, e incluso proporcionar información continua sobre el
estado de los pacientes, como signos tempranos de recaÃda, y hasta desarrollar nuevos
modelos de intervención. Aun asÃ, estos datos en crudo han de ser traducidos a datos
interpretables relacionados con la salud mental para conseguir un máximo rendimiento de
los mismos.
En salud mental existe una gran necesidad, y además hay mucho que ganar, de definir
cómo evaluar de forma continuada la evolución del estado mental de los pacientes en su
entorno cotidiano para ayudar en el tratamiento y seguimiento de los mismos por parte
de los profesionales sanitarios. En este ámbito, un enfoque basado en datos recopilados
desde sus smartphones puede ser valioso para recoger datos objetivos a largo plazo al
mismo tiempo que se acompaña de las autoevaluaciones utilizadas habitualmente. La
combinación de ambos tipos de datos puede ayudar a predecir los cambios en el estado
clÃnico de estos pacientes e investigar las relaciones causales sobre estos cambios (por
ejemplo, en aquellos que padecen trastornos afectivos).
Aunque la recogida de datos de forma pasiva tiene la ventaja de ser objetiva, también
implica varios retos. Por un lado, ciertos sensores generan grandes volúmenes de
datos, provocando un importante consumo de baterÃa. Además, el análisis de los datos
pasivos en crudo es complicado, y la recogida de ciertos tipos de datos puede interferir
con el fenotipo que se quiera analizar. No obstante, el machine learning o aprendizaje
automático, está predispuesto a resolver estas cuestiones y aportar avances en la medicina
personalizada aplicada a psiquiatrÃa.
Esta tesis tiene como objetivo avanzar en la investigación de los datos recogidos por
sensores de smartphones y wearables para la monitorización en salud mental. Para ello,
aplicamos métodos de aprendizaje automático supervisado y no supervisado para modelar y comprender la evolución de las enfermedades mentales basándonos en el fenotipo digital
de los pacientes. Estos resultados se comparan con las evaluaciones de los médicos en
las visitas de seguimiento, que proporcionan las etiquetas reales. Para aplicar estos métodos
hemos lidiado con datos provenientes de series temporales con alta dimensionalidad,
muestreados de forma regular e irregular, heterogéneos y, además, susceptibles a presentar
patrones de datos perdidos y/o distorsionados. Por lo tanto, los métodos desarrollados
deben ser resistentes a estas limitaciones y manejar adecuadamente los datos perdidos.
A lo largo de los distintos proyectos presentados en este trabajo, hemos utilizado
modelos probabilÃsticos de variables latentes para la imputación de datos y la extracción
de caracterÃsticas, como por ejemplo, Mixture Models (MM) y hidden Markov Models
(HMM). Estos modelos no supervisados pueden aprender incluso en presencia de datos
perdidos, marginalizando estos valores en función de las datos que sà han sido observados.
Una vez entrenados los modelos generativos en el conjunto de datos con valores
perdidos, pueden utilizarse para imputar dichos valores generando muestras. En primer
lugar, hay que encontrar el componente/estado más probable para cada muestra. Luego,
se muestrea de la distirbución más probable resultando en estimaciones de parámetros robustos
y válidos. Además, genera imputaciones explÃcitas que pueden ser tratadas como
resultados. Este proceso de imputación puede repetirse varias veces, creando múltiples
conjuntos de datos, con lo que se tiene en cuenta la incertidumbre de los valores imputados
y aumentándose asÃ, implÃcitamente, los datos. Además, estas imputaciones son
resistentes a desviaciones que puedan existir en los datos observados con respecto a la
distribución subyacente asumida y proporcionan estimaciones precisas incluso cuando la
falta de datos es elevada.
Dependiendo de las propiedades de los datos en cuestión, hemos usado métodos de extracción
de caracterÃsticas combinados con algoritmos clásicos de aprendizaje automático
o técnicas basadas en deep learning o aprendizaje profundo para el modelado temporal.
La finalidad de ambas opciones es ser capaces de predecir varios resultados de salud
mental/estado emocional, como la puntuación sobre el World Health Organisation Disability
Assessment Schedule (WHODAS 2.0), o las puntuaciones del generalised anxiety
disorder-7 (GAD-7) de pacientes psiquiátricos ambulatorios. Nos centramos principalmente
en modelos generalizados, es decir, no personalizados para cada paciente sino
explicativos para la mayorÃa, ya que el tamaño de muestras etiquetada por paciente es
limitado; sin embargo, en el caso de la predicción del estado de ánimo, puidmos aplicar
modelos personalizados.
Para que la integración de las máquinas y algoritmos dentro del flujo de trabajo clÃnico
sea aceptada, se requiere que los resultados sean interpretables. Por lo tanto, en este trabajo
también analizamos la importancia de las caracterÃsticas sacadas por cada algoritmo
en base a los valores de las explicaciones aditivas de Shapley (SHAP). Estos valores proporcionan
una visión general de las caracterÃsticas esenciales en los modelos de aprendizaje
automático designando el peso, positivo o negativo, de cada caracterÃstica en su
predictibilidad sobre la variable objetivo. Las soluciones aportadas en esta tesis, como tales, son pruebas de concepto, que requieren
una mayor validación clÃnica para poder ser desplegadas en el flujo de trabajo
clÃnico. Aun asÃ, los resultados son prometedores y sientan base para futuras investigaciones
y colaboraciones entre clÃnicos, pacientes y cientÃficos de datos. Éstas establecen
las guÃas para avanzar en las perspectivas de investigación futuras en la atención sanitaria
mental basada en la tecnologÃa.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: David RamÃrez GarcÃa.- Secretario: Alfredo Nazábal RenterÃa.- Vocal: MarÃa Luisa Barrigón Estéve
Photographic Mediation as a Mode of Production: Investigating the Agency of Commercial Institutions in Contemporary Vernacular Photography
This dissertation argues that to understand what is at stake in contemporary vernacular photography, it is vital to account for the commercial imperatives that are invested in our photographic apparatus. The vernacular is often seen as emerging from the milieu of everyday life, operating outside of institutional constraints. However, commercial institutions have always played a vital role in shaping the meaning and matter of vernacular photography, producing the extended network of devices and protocols through which photographic activity takes place. Vernacular photography should therefore be seen to encapsulate a series of complex negotiations between individual desires and commercial imperatives. Through an examination of three central case studies - Kodak, Snapchat and Ditto Labs - this thesis aims to elucidate how the productive potential of vernacular photography is instrumentalized as a means of generating value. Bringing together approaches from western Marxism with contemporary theories of networked media and photography, the argument is made that photographic mediation can be usefully framed as a mode of production. Photographic mediation, referring to the processual and material dynamics of photography, is employed to investigate the circuits of labour, value and desire that flow through our photographic apparatus. In performing this analysis, the concept of deterritorialization is applied as a way of understanding how photographic mediation has become more productive through destabilizing the boundaries between photography, subjectivity and the everyday. As photography proliferates and disperses into the rhythms and atmospheres that constitute daily life, it is increasingly imbricated into the performance and production of identities, relationships and desires. Under these circumstances, it becomes all the more vital that we recognize the role of commercial actors in shaping not only our photographic apparatus, but also our ways of being in, and relating to, the world
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