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Big data in health architecture

Asside Christian Djedouboum, Ado Adamou Abba Ari, Abdelhak Mourad Gueroui, Alidou Mohamadou, Zibouda Aliouat
Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity...
December 18, 2018: Sensors
Alramzana Nujum Navaz, Mohamed Adel Serhani, Nabeel Al-Qirim, Marton Gergely
BACKGROUND AND OBJECTIVES: Mobile and ubiquitous devices are everywhere, generating an exorbitant amount of data. New generations of healthcare systems are using mobile devices to continuously collect large amounts of different types of data from patients with chronic diseases. The challenge with such Mobile Big Data in general, is how to meet the growing performance demands of the mobile resources handling these tasks, while simultaneously minimizing their consumption. METHODS: This research proposes a scalable architecture for processing Mobile Big Data...
November 2018: Computer Methods and Programs in Biomedicine
Alberto Traverso, Johan van Soest, Leonard Wee, Andre Dekker
PURPOSE: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appealing, low-cost and noninvasive approach toward personalization. Machine learning algorithms could be trained over clinical "big data" to build prediction models for personalized therapy. To reach this goal, a scalable "big data" architecture for the medical domain becomes essential, based on data standardization to transform clinical data into FAIR (Findable, Accessible, Interoperable and Reusable) data...
October 2018: Medical Physics
Norbert Maggi, Roberta Gazzarata, Carmelina Ruggiero, Claudio Lombardo, Mauro Giacomini
INTRODUCTION: This article focuses on the integration of omics data in electronic health records and on interoperability aspects relating to big data analysis for precision medicine. METHODS: Omics data integration methods for electronic health record and for systems interoperability are considered, with special reference to the high number of specific software tools used to manage different aspects of patient treatment. This is an important barrier against the use of this integrated approach in daily clinical routine...
August 17, 2018: Tumori
Atilla Ergüzen, Erdal Erdal
Digital medical image usage is common in health services and clinics. These data have a vital importance for diagnosis and treatment; therefore, preservation, protection, and archiving of these data are a challenge. Rapidly growing file sizes differentiated data formats and increasing number of files constitute big data, which traditional systems do not have the capability to process and store these data. This study investigates an efficient middle layer platform based on Hadoop and MongoDB architecture using the state-of-the-art technologies in the literature...
2018: Journal of Healthcare Engineering
Naoual El Aboudi, Laila Benhlima
The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition...
2018: Advances in Bioinformatics
Márcio Freire Cruz, Carlos Arthur Mattos Teixeira Cavalcante, Sérgio Torres Sá Barretto
Health Level Seven (HL7) is one of the standards most used to centralize data from different vital sign monitoring systems. This solution significantly limits the data available for historical analysis, because it typically uses databases that are not effective in storing large volumes of data. In industry, a specific Big Data Historian, known as a Process Information Management System (PIMS), solves this problem. This work proposes the same solution to overcome the restriction on storing vital sign data. The PIMS needs a compatible communication standard to allow storing, and the one most commonly used is the OLE for Process Control (OPC)...
May 30, 2018: Journal of Medical Systems
Manuel Rodríguez-Martínez
Social media has become an important platform to gauge public opinion on topics related to our daily lives. In practice, processing these posts requires big data analytics tools since the volume of data and the speed of production overwhelm single-server solutions. Building an application to capture and analyze posts from social media can be a challenge simply because it requires combining a set of complex software tools that often times are tricky to configure, tune, and maintain. In many instances, the application ends up being an assorted collection of Java/Scala programs or Python scripts that developers cobble together to generate the data products they need...
June 2017: Proceedings. IEEE International Congress on Big Data
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, Wei Shi
BACKGROUND: In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations and owned by different entities. Past research has been focused on centralized algorithms, which assume the existence of a central data repository (database) which stores and can process the data from all participants. Such an architecture, however, can be impractical when data are not centrally located, it does not scale well to very large datasets, and introduces single-point of failure risks which could compromise the integrity and privacy of the data...
April 2018: International Journal of Medical Informatics
Abderrazak Sebaa, Fatima Chikh, Amina Nouicer, AbdelKamel Tari
The huge increases in medical devices and clinical applications which generate enormous data have raised a big issue in managing, processing, and mining this massive amount of data. Indeed, traditional data warehousing frameworks can not be effective when managing the volume, variety, and velocity of current medical applications. As a result, several data warehouses face many issues over medical data and many challenges need to be addressed. New solutions have emerged and Hadoop is one of the best examples, it can be used to process these streams of medical data...
February 19, 2018: Journal of Medical Systems
Panagiotis Katrakazas, Lyubov Trenkova, Josip Milas, Dario Brdaric, Dimitris Koutsouris
As Decision Support Systems start to play a significant role in decision making, especially in the field of public-health policy making, we present an initial attempt to formulate such a system in the concept of public health policy making for hearing loss related problems. Justification for the system's conceptual architecture and its key functionalities are presented. The introduction of the EVOTION DSS sets a key innovation and a basis for paradigm shift in policymaking, by incorporating relevant models, big data analytics and generic demographic data...
2017: Studies in Health Technology and Informatics
Philipp Urbauer, Maximilian Kmenta, Matthias Frohner, Alexander Mense, Stefan Sauermann
Interoperability is a key requirement for any IT-System to be future proof and cost efficient, due to the increasing interaction of IT-Systems in Healthcare. This feasibility study is part of a larger project focusing on the conceptualization and evaluation of interoperable and modular IT-Framework components for exchanging big data information sets. Hence, this project investigates the applicability of a standard based IT-Architecture for the integration of Personal Health Devices data and open data sources...
2017: Studies in Health Technology and Informatics
Mingrui Xia, Yong He
In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses...
October 15, 2017: NeuroImage
M Mazhar Rathore, Awais Ahmad, Anand Paul, Jiafu Wan, Daqiang Zhang
Healthy people are important for any nation's development. Use of the Internet of Things (IoT)-based body area networks (BANs) is increasing for continuous monitoring and medical healthcare in order to perform real-time actions in case of emergencies. However, in the case of monitoring the health of all citizens or people in a country, the millions of sensors attached to human bodies generate massive volume of heterogeneous data, called "Big Data." Processing Big Data and performing real-time actions in critical situations is a challenging task...
December 2016: Journal of Medical Systems
Shakoor Hajat, Ceri Whitmore, Christophe Sarran, Andy Haines, Brian Golding, Harriet Gordon-Brown, Anthony Kessel, Lora E Fleming
BACKGROUND: Improved data linkages between diverse environment and health datasets have the potential to provide new insights into the health impacts of environmental exposures, including complex climate change processes. Initiatives that link and explore big data in the environment and health arenas are now being established. OBJECTIVES: To encourage advances in this nascent field, this article documents the development of a web browser application to facilitate such future research, the challenges encountered to date, and how they were addressed...
January 1, 2017: Science of the Total Environment
Oresti Banos, Muhammad Bilal Amin, Wajahat Ali Khan, Muhammad Afzal, Maqbool Hussain, Byeong Ho Kang, Sungyong Lee
BACKGROUND: The provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people's conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems...
July 15, 2016: Biomedical Engineering Online
Stephan Velsko, Thomas Bates
Despite numerous calls for improvement, the US biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for "situational awareness" but is instead focused on the long-term goal of having true warning capabilities...
May 2016: Health Security
Sean Peisert, William Barnett, Eli Dart, James Cuff, Robert L Grossman, Edward Balas, Ari Berman, Anurag Shankar, Brian Tierney
OBJECTIVE: We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. MATERIALS AND METHODS: High-end networking, packet filter firewalls, network intrusion detection systems. RESULTS: We describe a "Medical Science DMZ" concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks...
November 2016: Journal of the American Medical Informatics Association: JAMIA
Daniel Racoceanu, Frédérique Capron
Being able to provide a traceable and dynamic second opinion has become an ethical priority for patients and health care professionals in modern computer-aided medicine. In this perspective, a semantic cognitive virtual microscopy approach has been recently initiated, the MICO project, by focusing on cognitive digital pathology. This approach supports the elaboration of pathology-compliant daily protocols dedicated to breast cancer grading, in particular mitotic counts and nuclear atypia. A proof of concept has thus been elaborated, and an extension of these approaches is now underway in a collaborative digital pathology framework, the FlexMIm project...
2016: Pathobiology: Journal of Immunopathology, Molecular and Cellular Biology
Bernd Schmeck, Wilhelm Bertrams, Xin Lai, Julio Vera
Lung diseases cause an enormous socioeconomic burden. Four of them are among the ten most important causes of deaths worldwide: Pneumonia has the highest death toll of all infectious diseases, lung cancer kills the most people of all malignant proliferative disorders, chronic obstructive pulmonary disease (COPD) ranks third in mortality among the chronic noncommunicable diseases, and tuberculosis is still one of the most important chronic infectious diseases. Despite all efforts, for example, by the World Health Organization and clinical and experimental researchers, these diseases are still highly prevalent and harmful...
2016: Methods in Molecular Biology
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