Particularities of visualisation of medical and wellness data through a digital patient avatar

In this work particularities of visualisation of medical and wellness data through a digital patient avatar are given from a standpoint of a proposed approach, under which data for a visualisation may be obtained from a variety of sources through defined interfaces, while end-user interfaces of distinct complexity and level of immersion into the model may be exposed to different categories of users. A short introduction of important medical data exchange standards, specifications and models is offered. A brief overview of projects relevant to a subject of this work is given. The proposed approach is presented along with examples of use-cases

Adoption of Big Data and AI methods to manage medication administration and intensive care environments

Artificial Intelligence (AI) has proven to be very helpful in different areas, including the medical field. One important parameter for healthcare professionals’ decision-making process is blood pressure, specifically mean arterial pressure (MAP). The application of AI in medicine, more specifically in Intensive Care Units (ICU) has the potential to improve the efficiency of healthcare and boost telemedicine operations with access to real-time predictions from remote locations. Operations that once required the presence of a healthcare professional, can be done at a distance, which facing the recent COVID-19 pandemic, proved to be crucial. This dissertation presents a solution to develop an AI system capable of accurately predicting MAP values. Many ICU patients suffer from sepsis or septic shock, and they can be identified by the need for vasopressors, such as noradrenaline, to keep their MAP above 65 mm Hg. The presented solution facilitates early interventions, thereby minimising the risk to patients. The current study reviews various machine learning (ML) models, training them to predict MAP values. One of the challenges is to see how the different models behave during their training process and choose the most promising one to test in a controlled environment. The dataset used to train the models contains identical data to the one generated by bedside monitors, which ensures that the models’ predictions align with real-world scenarios. The medical data generated is processed by a separate component that performs data cleaning, after which is directed to the application responsible for loading, classifying the data and utilising the ML model. To increase trust between healthcare professionals and the system to be developed, it is also intended to provide insights into how the results are achieved. The solution was integrated, for validation, with one of the telemedicine hubs deployed by the European project ICU4Covid through its CPS4TIC component.A Inteligência Artificial (IA) é muito útil em diferentes áreas, incluindo a saúde. Um parâmetro importante para a tomada de decisão dos profissionais de saúde é a pressão arterial, especificamente a pressão arterial média (PAM). A aplicação da IA na medicina, mais especificamente nas Unidades de Cuidados Intensivos (UCI), tem o potencial de melhorar a eficiência dos cuidados de saúde e impulsionar operações de telemedicina com acesso a previsões em tempo real a partir de locais remotos. As operações que exigiam a presença de um profissional de saúde, podem ser feitas à distância, o que, face à recente pandemia da COVID-19, se revelou crucial. Esta dissertação apresenta como solução um sistema de IA capaz de prever valores de PAM. Muitos pacientes nas UCI sofrem de sepse ou choque séptico, e podem ser identificados pela necessidade de vasopressores, como a noradrenalina, para manter a sua PAM acima dos 65 mm Hg. A solução apresentada facilita intervenções antecipadas, minimizando o risco para doentes. O estudo atual analisa vários modelos de machine learning (ML), e treina-os para preverem valores de PAM. Um desafio é ver o desempenho dos diferentes modelos durante o seu treino, e escolher o mais promissor para testar num ambiente controlado. O dataset utilizado para treinar os modelos contém dados idênticos aos gerados por monitores de cabeceira, o que assegura que as previsões se alinhem com cenários realistas. Os dados médicos gerados são processados por um componente separado responsável pela sua limpeza e envio para a aplicação responsável pelo seu carregamento, classificação e utilização do modelo ML. Para aumentar a confiança entre os profissionais de saúde e o sistema, pretende-se também fornecer uma explicação relativa à previsão dada. A solução foi integrada, para validação, com um dos centros de telemedicina implantado pelo projeto europeu ICU4Covid através da sua componente CPS4TIC

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

Arquitectura de un sistema integrado para diseño dirigido por modelos en el contexto de internet de las cosas con aplicaciones en medicina

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 14-10-20222Over the past few years, we have seen how processing and storage architectures become cheaper and more efficient, communication infrastructures become faster and more scalable, and many new ways of interacting with the world around us are being developed. Every day more devices are connected to the network, and the generation of data worldwide is growing exponentially. In this context, the Internet of Things promises to be the new technological revolution, as was the introduction of the network of networks or universal mobile accessibility in tis day...A lo largo de los últimos años hemos visto cómo las arquitecturas de procesamiento y almacenamiento se vuelven más baratas y eficientes, las infraestructuras de comunicación se hacen más rápidas y escalables, y se desarrollan multitud de nuevas formas de interactuar con el mundo que nos rodea. Cada día más dispositivos se conectan a la red, y la generación de datos a nivel mundal está creciendo exponencialmente. En este contexto, el Internet de las cosas promete ser la nueva revolución tecnológica, como en su día lo fue la introducción de la red de redes o la accesibilidad móvil universal...Fac. de InformáticaTRUEunpu

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field

Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness

2011 - 2012The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]XI n.s