1,721,104 research outputs found
One Digital Health for more FAIRness
No full textBackground One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention. Objectives This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup. Methods An integrative analysis of the literature was conducted to extract instances of the need—or of the eventual already existing deployment—of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework. Results A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention. Conclusions ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable
All for One, All at Once: A Pluggable and Referenceable Architecture for Monitoring Biophysical Parameters Across Intertwined Domains
No full textOne Digital Health: A Unified Framework for Future Health Ecosystems
No full textOne Digital Health is a proposed unified structure. The conceptual framework of the One Digital Health Steering Wheel is built around two keys (ie, One Health and digital health), three perspectives (ie, individual health and well-being, population and society, and ecosystem), and five dimensions (ie, citizens’ engagement, education, environment, human and veterinary health care, and Healthcare Industry 4.0). One Digital Health aims to digitally transform future health ecosystems, by implementing a systemic health and life sciences approach that takes into account broad digital technology perspectives on human health, animal health, and the management of the surrounding environment. This approach allows for the examination of how future generations of health informaticians can address the intrinsic complexity of novel health and care scenarios in digitally transformed health ecosystems. In the emerging hybrid landscape, citizens and their health data have been called to play a central role in the management of individual-level and population-level perspective data. The main challenges of One Digital Health include facilitating and improving interactions between One Health and digital health communities, to allow for efficient interactions and the delivery of near–real-time, data-driven contributions in systems medicine and systems ecology. However, digital health literacy; the capacity to understand and engage in health prevention activities; self-management; and collaboration in the prevention, control, and alleviation of potential problems are necessary in systemic, ecosystem-driven public health and data science research. Therefore, people in a healthy One Digital Health ecosystem must use an active and forceful approach to prevent and manage health crises and disasters, such as the COVID-19 pandemic
One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities
Full text linkSmart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other’s health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a “how-to” analysis of Tracy and Mego’s daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This “how-to” can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and “how-to's” to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management
Aide à l'exploration et à la découverte de relations dans des données de la Génomique Médicale Fonctionnelle
No full textData mining is an emerging area in the field of medical informatics research. Nowadays, clinical research protocols are no longer limited to collect only medical data, but they are also regarding other kinds of data, such as genomic data from cDNA microarrays. Currently, the approaches commonly used by biologists in this context explore a tiny part of the data based on a priori. Our work is based on automating the analysis process. Firstly, this Ph.D. dissertation focuses on the definition of a data workflow adapted to data that we deal with (bioclinical and genomics data). Secondly, outliers, due to the relative quality of data and sources of errors in analysis, are automatically identified thanks to a classification method. Finally, all these results will be presented easily to biologist experts. Experiments related to research in obesity medicine have been done and allowed to validate our Data Mining process and to discover biomarkers. Evaluations of use and usability have shown the benefits of our approach.La Fouille de Données est un domaine de recherche émergent en Informatique Médicale. Aujourd’hui, les protocoles de recherche clinique ne se contentent plus de collecter des données uniquement médicales, mais ils s’intéressent aussi aux données génétiques et génomiques. Les approches utilisées par les biologistes dans ce contexte n’étudient qu’une infime partie des données en se fondant sur des a priori. Nos travaux reposent sur l’automatisation de ce processus d’analyse. Premièrement, cette thèse s’intéressera à la mise en place d’un flux de données adapté aux données que nous souhaitons traiter (données biocliniques et issues de puces à ADNc). Ensuite, les valeurs singulières, dues à la qualité relative des données et sources d’erreurs, seront identifiées automatiquement grâce à une méthode de classification. Enfin, l’ensemble de ces résultats sera présenté de manière accessible aux experts. Des expérimentations ont été menées dans le domaine de l’étude des Obésités et ont permis de valider notre processus et de découvrir des biomarqueurs. Une analyse globale d’usage et d’utilisabilité a montré l’intérêt de notre approche
Aide à l'exploration et à la découverte de relations dans des données de la Génomique Médicale Fonctionnelle
No full textData mining is an emerging area in the field of medical informatics research. Nowadays, clinical research protocols are no longer limited to collect only medical data, but they are also regarding other kinds of data, such as genomic data from cDNA microarrays. Currently, the approaches commonly used by biologists in this context explore a tiny part of the data based on a priori. Our work is based on automating the analysis process. Firstly, this Ph.D. dissertation focuses on the definition of a data workflow adapted to data that we deal with (bioclinical and genomics data). Secondly, outliers, due to the relative quality of data and sources of errors in analysis, are automatically identified thanks to a classification method. Finally, all these results will be presented easily to biologist experts. Experiments related to research in obesity medicine have been done and allowed to validate our Data Mining process and to discover biomarkers. Evaluations of use and usability have shown the benefits of our approach.La Fouille de Données est un domaine de recherche émergent en Informatique Médicale. Aujourd’hui, les protocoles de recherche clinique ne se contentent plus de collecter des données uniquement médicales, mais ils s’intéressent aussi aux données génétiques et génomiques. Les approches utilisées par les biologistes dans ce contexte n’étudient qu’une infime partie des données en se fondant sur des a priori. Nos travaux reposent sur l’automatisation de ce processus d’analyse. Premièrement, cette thèse s’intéressera à la mise en place d’un flux de données adapté aux données que nous souhaitons traiter (données biocliniques et issues de puces à ADNc). Ensuite, les valeurs singulières, dues à la qualité relative des données et sources d’erreurs, seront identifiées automatiquement grâce à une méthode de classification. Enfin, l’ensemble de ces résultats sera présenté de manière accessible aux experts. Des expérimentations ont été menées dans le domaine de l’étude des Obésités et ont permis de valider notre processus et de découvrir des biomarqueurs. Une analyse globale d’usage et d’utilisabilité a montré l’intérêt de notre approche
Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments
No full textBUFF4L.0: at the crossroads between Veterinary and Engineering Sciences in the Industry 4.0 era
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