1,239 research outputs found

    Health data in cloud environments

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    The process of provisioning healthcare involves massive healthcare data which exists in different forms on disparate data sources and in different formats. Consequently, health information systems encounter interoperability problems at many levels. Integrating these disparate systems requires the support at all levels of a very expensive infrastructures. Cloud computing dramatically reduces the expense and complexity of managing IT systems. Business customers do not need to invest in their own costly IT infrastructure, but can delegate and deploy their services effectively to Cloud vendors and service providers. It is inevitable that electronic health records (EHRs) and healthcare-related services will be deployed on cloud platforms to reduce the cost and complexity of handling and integrating medical records while improving efficiency and accuracy. The paper presents a review of EHR including definitions, EHR file formats, structures leading to the discussion of interoperability and security issues. The paper also presents challenges that have to be addressed for realizing Cloudbased healthcare systems: data protection and big health data management. Finally, the paper presents an active data model for housing and protecting EHRs in a Cloud environment

    Wiki-health: from quantified self to self-understanding

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    Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

    Medical Image Management by NoSQL Database

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    Over the past three decades, medical imaging technology has revolutionized the healthcare sector. It enables medical professionals to detect disease early and improve patient outcomes. Large amounts of semi-structured and unstructured picture data that lack a set schema are produced as health records. Better data models for storage and retrieval are required. Big Data may be stored and retrieved much more easily with NoSQL (Not merely SQL) databases. Hence, the suggestion is to store medical health records in a NoSQL database. This essay aims to contrast several NoSQL databases from the perspective of image technology

    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

    How Technology May be Used for Future Disease Prediction: A Systematic Literature Review

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    Exasperated by the current pandemic, our healthcare system continues to struggle with the accuracy and effectiveness of disease treatments. However, despite these growing challenges, technological advancements have aided potential disease prediction. There has been a positive correlation between utilizing technologies and leveraging them for disease predictions. Thanks to our continued reliance and technological advancement, current research shows that it has many viable options to aid the healthcare field. This systematic review looks at the current state of how technologies have been and can be used to improve healthcare
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