387 research outputs found

    Electronic Health Record Exchange Using a Peer to Peer

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    These days when a patient go to the hospital, most of the times we’re forced to do the checks that may have previosly been done in other hospitals, such as checking blood types, hereditary disease, and so forth. Or maybe patients just moved from an area whether you need to do re-checking. Why not have a container that can accomodate the data, so the things mentioned above can be reduced. There should be an electronic health record system that stores all the patient’s health history. Therefore we try to discuss a system with the method of peer-to-peer that is developed until today. We will shows the dierences of each method architecture peer-to-peer, and will also illustrate it in a example case. This is expected to help patients and other health institution in making decisions and optimize the health services

    Sharing and viewing segments of electronic patient records service (SVSEPRS) using multidimensional database model

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    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The concentration on healthcare information technology has never been determined than it is today. This awareness arises from the efforts to accomplish the extreme utilization of Electronic Health Record (EHR). Due to the greater mobility of the population, EHR will be constructed and continuously updated from the contribution of one or many EPRs that are created and stored at different healthcare locations such as acute Hospitals, community services, Mental Health and Social Services. The challenge is to provide healthcare professionals, remotely among heterogeneous interoperable systems, with a complete view of the selective relevant and vital EPRs fragments of each patient during their care. Obtaining extensive EPRs at the point of delivery, together with ability to search for and view vital, valuable, accurate and relevant EPRs fragments can be still challenging. It is needed to reduce redundancy, enhance the quality of medical decision making, decrease the time needed to navigate through very high number of EPRs, which consequently promote the workflow and ease the extra work needed by clinicians. These demands was evaluated through introducing a system model named SVSEPRS (Searching and Viewing Segments of Electronic Patient Records Service) to enable healthcare providers supply high quality and more efficient services, redundant clinical diagnostic tests. Also inappropriate medical decision making process should be avoided via allowing all patients‟ previous clinical tests and healthcare information to be shared between various healthcare organizations. Multidimensional data model, which lie at the core of On-Line Analytical Processing (OLAP) systems can handle the duplication of healthcare services. This is done by allowing quick search and access to vital and relevant fragments from scattered EPRs to view more comprehensive picture and promote advances in the diagnosis and treatment of illnesses. SVSEPRS is a web based system model that helps participant to search for and view virtual EPR segments, using an endowed and well structured Centralised Multidimensional Search Mapping (CMDSM). This defines different quantitative values (measures), and descriptive categories (dimensions) allows clinicians to slice and dice or drill down to more detailed levels or roll up to higher levels to meet clinicians required fragment

    Securely sharing dynamic medical information in e-health

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    This thesis has introduced an infrastructure to share dynamic medical data between mixed health care providers in a secure way, which could benefit the health care system as a whole. The study results of the universally data sharing into a varied patient information system prototypes

    Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

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    In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

    Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

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    Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance

    A patient agent controlled customized blockchain based framework for internet of things

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    Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

    Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions

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    Blockchain-based drug supply management (DSM) requires powerful security and privacy procedures for high-level authentication, interoperability, and medical record sharing. Researchers have shown a surprising interest in Internet of Things (IoT)-based smart cities in recent years. By providing a variety of intelligent applications, such as intelligent transportation, industry 4.0, and smart financing, smart cities (SC) can improve the quality of life for their residents. Blockchain technology (BCT) can allow SC to offer a higher standard of security by keeping track of transactions in an immutable, secure, decentralized, and transparent distributed ledger. The goal of this study is to systematically explore the current state of research surrounding cutting-edge technologies, particularly the deployment of BCT and the IoT in DSM and SC. In this study, the defined keywords “blockchain”, “IoT”, drug supply management”, “healthcare”, and “smart cities” as well as their variations were used to conduct a systematic search of all relevant research articles that were collected from several databases such as Science Direct, JStor, Taylor & Francis, Sage, Emerald insight, IEEE, INFORMS, MDPI, ACM, Web of Science, and Google Scholar. The final collection of papers on the use of BCT and IoT in DSM and SC is organized into three categories. The first category contains articles about the development and design of DSM and SC applications that incorporate BCT and IoT, such as new architecture, system designs, frameworks, models, and algorithms. Studies that investigated the use of BCT and IoT in the DSM and SC make up the second category of research. The third category is comprised of review articles regarding the incorporation of BCT and IoT into DSM and SC-based applications. Furthermore, this paper identifies various motives for using BCT and IoT in DSM and SC, as well as open problems and makes recommendations. The current study contributes to the existing body of knowledge by offering a complete review of potential alternatives and finding areas where further research is needed. As a consequence of this, researchers are presented with intriguing potential to further create decentralized DSM and SC apps as a result of a comprehensive discussion of the relevance of BCT and its implementation.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

    Proceedings of the ECIR2010 workshop on information access for personal media archives (IAPMA2010), Milton Keynes, UK, 28 March 2010

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    Towards e-Memories: challenges of capturing, summarising, presenting, understanding, using, and retrieving relevant information from heterogeneous data contained in personal media archives. This is the proceedings of the inaugural workshop on “Information Access for Personal Media Archives”. It is now possible to archive much of our life experiences in digital form using a variety of sources, e.g. blogs written, tweets made, social network status updates, photographs taken, videos seen, music heard, physiological monitoring, locations visited and environmentally sensed data of those places, details of people met, etc. Information can be captured from a myriad of personal information devices including desktop computers, PDAs, digital cameras, video and audio recorders, and various sensors, including GPS, Bluetooth, and biometric devices. In this workshop research from diverse disciplines was presented on how we can advance towards the goal of effective capture, retrieval and exploration of e-memories

    Assured information sharing for ad-hoc collaboration

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    Collaborative information sharing tends to be highly dynamic and often ad hoc among organizations. The dynamic natures and sharing patterns in ad-hoc collaboration impose a need for a comprehensive and flexible approach to reflecting and coping with the unique access control requirements associated with the environment. This dissertation outlines a Role-based Access Management for Ad-hoc Resource Shar- ing framework (RAMARS) to enable secure and selective information sharing in the het- erogeneous ad-hoc collaborative environment. Our framework incorporates a role-based approach to addressing originator control, delegation and dissemination control. A special trust-aware feature is incorporated to deal with dynamic user and trust management, and a novel resource modeling scheme is proposed to support fine-grained selective sharing of composite data. As a policy-driven approach, we formally specify the necessary pol- icy components in our framework and develop access control policies using standardized eXtensible Access Control Markup Language (XACML). The feasibility of our approach is evaluated in two emerging collaborative information sharing infrastructures: peer-to- peer networking (P2P) and Grid computing. As a potential application domain, RAMARS framework is further extended and adopted in secure healthcare services, with a unified patient-centric access control scheme being proposed to enable selective and authorized sharing of Electronic Health Records (EHRs), accommodating various privacy protection requirements at different levels of granularity
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