Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Pervasive intelligent decision support in critical health care

Tese de doutoramento (área de especialização em Tecnologias e Sistemas de Informação)Intensive Care Units (ICU) are recognized as being critical environments, due to the fact that patients admitted to these units typically find themselves in situations of organ failure or serious health conditions. ICU professionals (doctors and nurses) dedicate most of their time taking care for the patients, relegating to a second plan all documentation tasks. Tasks such as recording vital signs, treatment planning and calculation of indicators, are only performed when patients are in a stable clinical condition. These records can occur with a lag of several hours. Since this is a critical environment, the Process of Decision Making (PDM) has to be fast, objective and effective. Any error or delay in the implementation of a particular decision may result in the loss of a human life. Aiming to minimize the human effort in bureaucratic processes and improve the PDM, dematerialization of information is required, eliminating paper-based recording and promoting an automatic registration of electronic and real-time data of patients. These data can then be used as a complement to the PDM, e.g. in Decision Support Systems that use Data Mining (DM) models. At the same time it is important for PDM to overcome barriers of time and space, making the platforms as universal as possible, accessible anywhere and anytime, regardless of the devices used. In this sense, it has been observed a proliferation of pervasive systems in healthcare. These systems are focused on providing healthcare to anyone, anytime and anywhere by removing restrictions of time and place, increasing both the coverage and quality of health care. This approach is mainly based on information that is stored and available online. With the aim of supporting the PDM a set of tests were carried out using static DM models making use of data that had been collected and entered manually in Euricus database. Preliminary results of these tests showed that it was possible to predict organ failure and outcome of a patient using DM techniques considering a set of physiological and clinical variables as input. High rates of sensitivity were achieved: Cardiovascular - 93.4%; Respiratory - 96.2%; Renal - 98.1%; Liver - 98.3%; hematologic - 97.5%; and Outcome and 98.3%. Upon completion of this study a challenge emerged: how to achieve the same results but in a dynamic way and in real time? A research question has been postulated as: "To what extent, Intelligent Decision Support Systems (IDSS) may be appropriate for critical clinical settings in a pervasive way? “. Research work included: 1. To percept what challenges a universal approach brings to IDSS, in the context of critical environments; 2. To understand how pervasive approaches can be adapted to critical environments; 3. To develop and test predictive models for pervasive approaches in health care. The main results achieved in this work made possible: 1. To prove the adequacy of pervasive approach in critical environments; 2. To design a new architecture that includes the information requirements for a pervasive approach, able to automate the process of knowledge discovery in databases; 3. To develop models to support pervasive intelligent decision able to act automatically and in real time. To induce DM ensembles in real time able to adapt autonomously in order to achieve predefined quality thresholds (total error = 85 % and accuracy > = 60 %). Main contributions of this work include new knowledge to help overcoming the requirements of a pervasive approach in critical environments. Some barriers inherent to information systems, like the acquisition and processing of data in real time and the induction of adaptive ensembles in real time using DM, have been broken. The dissemination of results is done via devices located anywhere and anytime.As Unidades de Cuidados Intensivos (UCIs) são conhecidas por serem ambientes críticos, uma vez que os doentes admitidos nestas unidades encontram-se, tipicamente, em situações de falência orgânica ou em graves condições de saúde. Os profissionais das UCIs (médicos e enfermeiros) dedicam a maioria do seu tempo no cuidado aos doentes, relegando para segundo plano todas as tarefas relacionadas com documentação. Tarefas como o registo dos sinais vitais, o planeamento do tratamento e o cálculo de indicadores são apenas realizados quando os doentes se encontram numa situação clínica estável. Devido a esta situação, estes registos podem ocorrer com um atraso de várias horas. Dado que este é um ambiente crítico, o Processo de Tomada de Decisão (PTD) tem de ser rápido, objetivo e eficaz. Qualquer erro ou atraso na implementação de uma determinada decisão pode resultar na perda de uma vida humana. Com o intuito de minimizar os esforços humanos em processos burocráticos e de otimizar o PTD, é necessário proceder à desmaterialização da informação, eliminando o registo em papel, e promover o registo automático e eletrónico dos dados dos doentes obtidos em tempo real. Estes dados podem, assim, ser usados com um complemento ao PTD, ou seja, podem ser usados em Sistemas de Apoio à Decisão que utilizem modelos de Data Mining (DM). Ao mesmo tempo, é imperativo para o PTD superar barreiras ao nível de tempo e espaço, desenvolvendo plataformas tão universais quanto possíveis, acessíveis em qualquer lugar e a qualquer hora, independentemente dos dispositivos usados. Nesse sentido, tem-se verificado uma proliferação dos sistemas pervasive na saúde. Estes sistemas focam-se na prestação de cuidados de saúde a qualquer pessoa, a qualquer altura e em qualquer lugar através da eliminação das restrições ao nível do tempo e espaço, aumentando a cobertura e a qualidade na área da saúde. Esta abordagem é, principalmente, baseada em informações que estão armazenadas disponíveis online. Com o objetivo de suportar o PTD, foi realizado um conjunto de testes com modelos de DM estáticos, recorrendo a dados recolhidos e introduzidos manualmente na base de dados “Euricus”. Os resultados preliminares destes testes mostraram que era possível prever a falência orgânica ou a alta hospitalar de um doente, através de técnicas de DM utilizando como valores de entrada um conjunto de variáveis clínicas e fisiológicas. Nos testes efetuados, foram obtidos elevados níveis de sensibilidade: cardiovascular - 93.4%; respiratório - 96.2%; renal - 98.1%; hepático - 98.3%; hematológico - 97.5%; e alta hospitalar - 98.3%. Com a finalização deste estudo, observou-se o aparecimento de um novo desafio: como alcançar os mesmos resultados mas em modo dinâmico e em tempo real? Uma questão de investigação foi postulada: “Em que medida os Sistemas de Apoio à Decisão Inteligentes (SADIs) podem ser adequados às configurações clínicas críticas num modo pervasive?”. Face ao exposto, o trabalho de investigação inclui os seguintes pontos: 1. Perceber quais os desafios que uma abordagem universal traz para os SADIs, no contexto dos ambientes críticos; 2. Compreender como as abordagens pervasive podem ser adaptadas aos ambientes críticos; 3. Desenvolver e testar modelos de previsão para abordagens pervasive na área da saúde. Os principais resultados alcançados neste trabalho tornaram possível: 1. Provar a adequação da abordagem pervasive em ambientes críticos; 2. Conceber uma nova arquitetura que inclui os requisitos de informação para uma abordagem pervasive, capaz de automatizar o processo de descoberta de conhecimento em base de dados; 3. Desenvolver modelos de suporte à decisão inteligente e pervasive, capazes de atuar automaticamente e em tempo real. Induzir ensembles DM em tempo real, capazes de se adaptarem de forma autónoma, com o intuito de alcançar as medidas de qualidade pré-definidas (erro total = 85 % e acuidade> = 60 %). As principais contribuições deste trabalho incluem novos conhecimentos para ajudar a ultrapassar as exigências de uma abordagem pervasive em ambientes críticos. Algumas barreiras inerentes aos sistemas de informação, como a aquisição e o processamento de dados em tempo real e a indução de ensembles adaptativos em tempo real utilizando DM, foram transpostas. A divulgação dos resultados é feita através de dispositivos localizados, em qualquer lugar e a qualquer hora.Intensive Care Units (ICU) are recognized as being critical environments, due to the fact that patients admitted to these units typically find themselves in situations of organ failure or serious health conditions. ICU professionals (doctors and nurses) dedicate most of their time taking care for the patients, relegating to a second plan all documentation tasks. Tasks such as recording vital signs, treatment planning and calculation of indicators, are only performed when patients are in a stable clinical condition. These records can occur with a lag of several hours. Since this is a critical environment, the Process of Decision Making (PDM) has to be fast, objective and effective. Any error or delay in the implementation of a particular decision may result in the loss of a human life. Aiming to minimize the human effort in bureaucratic processes and improve the PDM, dematerialization of information is required, eliminating paper-based recording and promoting an automatic registration of electronic and real-time data of patients. These data can then be used as a complement to the PDM, e.g. in Decision Support Systems that use Data Mining (DM) models. At the same time it is important for PDM to overcome barriers of time and space, making the platforms as universal as possible, accessible anywhere and anytime, regardless of the devices used. In this sense, it has been observed a proliferation of pervasive systems in healthcare. These systems are focused on providing healthcare to anyone, anytime and anywhere by removing restrictions of time and place, increasing both the coverage and quality of health care. This approach is mainly based on information that is stored and available online. With the aim of supporting the PDM a set of tests were carried out using static DM models making use of data that had been collected and entered manually in Euricus database. Preliminary results of these tests showed that it was possible to predict organ failure and outcome of a patient using DM techniques considering a set of physiological and clinical variables as input. High rates of sensitivity were achieved: Cardiovascular - 93.4%; Respiratory - 96.2%; Renal - 98.1%; Liver - 98.3%; hematologic - 97.5%; and Outcome and 98.3%. Upon completion of this study a challenge emerged: how to achieve the same results but in a dynamic way and in real time? A research question has been postulated as: "To what extent, Intelligent Decision Support Systems (IDSS) may be appropriate for critical clinical settings in a pervasive way? “. Research work included: 1. To percept what challenges a universal approach brings to IDSS, in the context of critical environments; 2. To understand how pervasive approaches can be adapted to critical environments; 3. To develop and test predictive models for pervasive approaches in health care. The main results achieved in this work made possible: 1. To prove the adequacy of pervasive approach in critical environments; 2. To design a new architecture that includes the information requirements for a pervasive approach, able to automate the process of knowledge discovery in databases; 3. To develop models to support pervasive intelligent decision able to act automatically and in real time. To induce DM ensembles in real time able to adapt autonomously in order to achieve predefined quality thresholds (total error = 85 % and accuracy > = 60 %). Main contributions of this work include new knowledge to help overcoming the requirements of a pervasive approach in critical environments. Some barriers inherent to information systems, like the acquisition and processing of data in real time and the induction of adaptive ensembles in real time using DM, have been broken. The dissemination of results is done via devices located anywhere and anytime

Improving quality of services in maternity care triage system

The main objectives in hospital triages are to improve the quality of care and reduce the risks associated to the waiting time in emergency care. Thus, an efficient triage is a good way to avoid some future problems and how much quicker it is, more the patient can benefit. The most common triage system is the Manchester Triage System (MTS). MTS is a reliable system focused in the hospital emergency department. However, its use is more suitable for more widespread medical emergencies and not for specialized cases as is Maternity Care and Gynecological and Obstetrics emergencies. To overcome these limitations an alternative pre-triage system, integrated into an intelligent decision support system, was developed in order to better characterize the patient and correctly defined the patient as urgent or not. This system allows increasing patient’s safety, especially women who need immediate care. This paper presents the main results and the workflow describing the decision process in real time in the emergency department, when the women are submitted to the triage process and they are identified possible evolution points of this system.This work is funded by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEstOE/EEI/UI0752/2014 and PEst-OE/EEI/UI0319/201

Towards pervasive computing in health care – A literature review

<p>Abstract</p> <p>Background</p> <p>The evolving concepts of pervasive computing, ubiquitous computing and ambient intelligence are increasingly influencing health care and medicine. Summarizing published research, this literature review provides an overview of recent developments and implementations of pervasive computing systems in health care. It also highlights some of the experiences reported in deployment processes.</p> <p>Methods</p> <p>There is no clear definition of pervasive computing in the current literature. Thus specific inclusion criteria for selecting articles about relevant systems were developed. Searches were conducted in four scientific databases alongside manual journal searches for the period of 2002 to 2006. Articles included present prototypes, case studies and pilot studies, clinical trials and systems that are already in routine use.</p> <p>Results</p> <p>The searches identified 69 articles describing 67 different systems. In a quantitative analysis, these systems were categorized into project status, health care settings, user groups, improvement aims, and systems features (i.e., component types, data gathering, data transmission, systems functions). The focus is on the types of systems implemented, their frequency of occurrence and their characteristics. Qualitative analyses were performed of deployment issues, such as organizational and personnel issues, privacy and security issues, and financial issues. This paper provides a comprehensive access to the literature of the emerging field by addressing specific topics of application settings, systems features, and deployment experiences.</p> <p>Conclusion</p> <p>Both an overview and an analysis of the literature on a broad and heterogeneous range of systems are provided. Most systems are described in their prototype stages. Deployment issues, such as implications on organization or personnel, privacy concerns, or financial issues are mentioned rarely, though their solution is regarded as decisive in transferring promising systems to a stage of regular operation. There is a need for further research on the deployment of pervasive computing systems, including clinical studies, economic and social analyses, user studies, etc.</p

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

Human Data Model: Improving Programmability of Health and Well-Being Data for Enhanced Perception and Interaction

Peer reviewe

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201