azure cloud hospital laboratory
39097834
Comparing Google Cloud and Microsoft Azure Platforms for Undergraduate Laboratory Use
Knowing the advantages of public cloud services is important for undergraduate students that will work in the IT domain. The study of cloud technology should cover not only the theoretical aspects but should also give hands on access and experience with the management interface. Two important players on this market offer access to services as a free trial that allows testing and light cloud platform usage: Microsoft Azure and Google Cloud. This paper compares these free services and their usability in an undergraduate laboratory at the Computer Science specialization and proposes a laboratory structure that should cover this process of investigation.
SOCO CISIS ICEUTE
Valeriu Manuel Ionescu Jose Manuel Lopez-Guede
226267433
A Rapid Prototyping Approach in Industry4.0 based on Secure Hardware and Azure Sphere Cloud
The concept of Industry 4.0 is mainly based on the concept of Digital Transformation which involves all main procedures and activities in a company. Fundamental elements of this transformation are the technologies enabling the Internet of Things and the potential offered by PaaS services such as Microsoft Azure platform. Currently, the industrial context is strongly interested in stimulating the experimentation of smart, safe, low cost and low power systems. This work aims to describe the potential in terms of rapid prototyping of smart industrial systems based on MT3620 MCU and Microsoft Azure Sphere platform. To better understand their potential, a simple use case has been defined and implemented. Furthermore, a functional validation of the implemented system has been carried out in the laboratory and the main first and partial results have been discussed. Finally, main benefits in the use of these innovative technologies in Industry4.0 sector have been briefly illustrated.
2020 5th International Conference on Smart and Sustainable Technologies (SpliTech)
Luca Cremona Nicola Carrus Ilaria Sergi Luigi Patrono
51940721
Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System
Background Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross system surveillance and control system for infectious diseases. Objective The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. Results The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8% 96.6% and 97.4% respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1% respectively. After the reported data were integrated with patients' diagnostic results in their electronic medical records (EMRs) the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1% respectively. Conclusions The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases' clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.
Journal of medical Internet research
Mei-Hua Wang Han-Kun Chen Min-Huei Hsu Hui-Chi Wang Yu-Ting Yeh
218521776
Blockchain Based Healthcare Workflow for Tele Medical Laboratory in Federated Hospital IoT Clouds
In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid 19 virus, the need of tele healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology. In particular, both public and hybrid Blockchain scenarios are discussed and assessed using the Ethereum platform.
Sensors
Antonio Celesti Armando Ruggeri Maria Fazio Antonino Galletta Massimo Villari Agata Romano
49640080
Deploying Cloud Computing in the Greek Healthcare System: A Modern Development Proposal Incorporating Clinical and Laboratory Data
Cloud computing is a reality in most business sectors. Hospitals have been more reluctant to adopt cloud technology due to strict data security regulations. Cloud could provide economies of scale reducing Information Technology spending in the Greek state owned hospitals, while giving the opportunity to the hospitals to upgrade their profile offering web based services. We propose a simple, robust and easy to apply approach for the Greek hospitals, focusing on clinical and laboratory data in order to move to the cloud environment. To the best of our knowledge, there is no other study regarding the adoption of cloud infrastructure in the Greek hospital sector. This innovative method could transform the business model of the hospitals.
ICIMTH
Markos Nikolopoulos Irene Karampela Evangelos Tzortzis Maria Dalamaga
53428368
Laboratory Environment Monitoring: Implementation Experience and Field Study in a Tertiary General Hospital
Objectives To successfully introduce an Internet of Things (IoT) system in the hospital environment, this study aimed to identify issues that should be considered while implementing an IoT based on a user demand survey and practical experiences in implementing IoT environment monitoring systems. Methods In a field test, two types of IoT monitoring systems (on premises and cloud) were used in Department of Laboratory Medicine and tested for approximately 10 months from June 16, 2016 to April 30, 2017. Information was collected regarding the issues that arose during the implementation process. Results A total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility. Conclusions It is expected that, with sufficient consideration of the various issues derived from this study, IoT monitoring systems can be applied to other areas, such as device interconnection, remote patient monitoring, and equipment/environmental monitoring.
Healthcare informatics research
Seungjin Kang Hyunyoung Baek Sunhee Jun Soo-Dong Choi Hee Hwang Sooyoung Yoo
49421378
Scale Down Methods for Optimizing Resource Allocation In Providing Virtual Laboratory Environment by Cloud Computing
Nowadays, Cloud Computing plays a vital role in providing virtual infrastructure for computing demands and services such as Amazon EC2, Google Cloud, Azure. In addition, Cloud Computing has potential for supporting experimental environment as Virtual Laboratory in terms of education. Instead of the real laboratory, Virtual Laboratory can save deploying time and cost. With the model that we are implementing on our High Performance Computing system, lecturers are considered as users who request the virtual resources for experimental environment. Hence, the problem that we pose is how to optimize the mechanism of allocating resources efficiently for both the user side and the server side. This paper contributes two scale down algorithms which serve Virtual Laboratory users and optimize the scheduler of resource allocation on server side for multiple courses. The results illustrate that our aforementioned algorithms not only save costs by scaling down virtual resources but also satisfy students experiment in using the Virtual Laboratory.
2017 International Conference on Advanced Computing and Applications (ACOMP)
Ban Tien Nguyen Minh Thanh Chung Nguyen Quang-Hung Manh-Thin Nguyen Nam Thoai
221971032
Machine Learning Algorithms for Active Monitoring of High Performance Computing as a Service (HPCaaS) Cloud Environments
Cloud computing provides ubiquitous and on demand access to vast reconfigurable resources that can meet any computational need. Many service models are available, but the Infrastructure as a Service (IaaS) model is particularly suited to operate as a high performance computing (HPC) platform, by networking large numbers of cloud computing nodes. We used the Pacific Northwest National Laboratory (PNNL) cloud computing environment to perform our experiments. A number of cloud computing providers such as Amazon Web Services, Microsoft Azure, or IBM Cloud, offer flexible and scalable computing resources. This paper explores the viability identifying types of engineering applications running on a cloud infrastructure configured as an HPC platform using privacy preserving features as input to statistical models. The engineering applications considered in this work include MCNP6, a radiation transport code developed by Los Alamos National Laboratory, OpenFOAM, an open source computational fluid dynamics code, and CADO NFS, a numerical implementation of the general number field sieve algorithm used for prime number factorization. Our experiments use the OpenStack cloud management tool to create a cloud HPC environment and the privacy preserving Ceilometer billing meters as classification features to demonstrate identification of these applications.
ArXiv
Gianluca Longoni Ryan R LaMothe Jeremy Teuton Mark T Greaves Nicole Nichols William Smith
218669643
Home based HPV self sampling assisted by a cloud based electronic data system: Lessons learnt from a pilot community cervical cancer screening campaign in rural Ethiopia
Primary HPV testing and triage of HPV positive women is an effective cervical cancer screening strategy. Such a multi visit screening algorithm is also promising for community based screening in resource poor communities, provided a robust tracking system is in place. A cervical cancer screening campaign was conducted in a rural community in Ethiopia. All women aged 25 65 years were offered genital self sampling using the Evalyn Brush(r) Samples were HPV DNA tested at a central laboratory. Key indicators were captured on tablet computers and linked by a cloud based information system. HPV positive women were examined at the local clinic using portable colposcopy, p16/Ki 67 dual stain cytology and biopsy examination. CIN2+ women were referred for LEEP to the referral hospital. Of 749 enumerated age eligible women 634 (85% (95% CI 82 88) consented to screening, 429 samples were adequate for HPV testing, giving a total testing coverage of 57% (95% CI 53 62) The hrHPV prevalence was 14% (95% CI 5 22) 72% (95% CI 60 84) attended the clinic for a triage examination. Home based HPV DNA self sampling and clinic based triage assisted by cloud based information technology is feasible in rural Ethiopia. Key components of such strategy are broad community awareness, high competency of community workers, and establishment of an adequate self sampling and HPV DNA testing platform.
Papillomavirus research
Felix Jede Theresa Brandt Molla Gedefaw Solomon Berhe Wubneh Tamrat Befekadu Abebe Brhanu Teka Kassahun Alemu Binyam Tilahun Temesgen Azemeraw Abebaw Gebeyehu Dietmar Schmidt Aleksandra Pesic Andreas Martin Kaufmann Bewketu Abebe Zelalem Ayichew Michael Byczkowski Timote Vaucher Heike Sartor Gashaw Andargie Till W Barnighausen Magnus von Knebel Doeberitz Hermann Bussmann
57746753
Architecture for Software as a Service (SaaS) Model of CBIR on Hybrid Cloud of Microsoft Azure
Abstract In various areas of government, academia, hospitals and commerce large collections of digital images are produced. Many of these collections are the merchandise of digitizing existing collections of analogue drawings, photographs, paintings and prints. Generally the only way of searching these collections was by basically browsing or keyword indexing. Digital images databases however, open the way to content based searching. Content Based Image Retrieval (CBIR) is concerned with the retrieval of images similar to a specified image, from an image repository. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. This paper proposes a system that can be used for retrieving images related to a query image from a large set of distinct images. It follows an image segmentation based approach to extract the different features present in an image. The above features which can be stored in vectors called feature vectors and therefore these are compared to the feature vectors of query image and the image information is sorted in decreasing order of similarity. The processing of the same is done on cloud. The CBIR system is an application built on Windows Azure platform. It is a parallel processing problem where a large set of images have to be operated upon to rank them based on a similarity to a provided query image by the user. Numerous instances of the algorithm run on the virtual machines provided in the Microsoft data centers, which run Windows Azure. Windows Azure Stack is the operating system for the cloud by Microsoft Incorporation. Windows azure Stack is responsible for creating ideal hybrid architecture
M Meena Anamika Singh Vinayak Ashok Bharadi