Cloud Computing In Healthcare Research Articles

Detecting the Type of Attack in Healthcare Cloud Computing System Using Termite Deep Belief Neural Network (TDBNN) for Future Smart Cities

Abstract With the popularity of smart electronic devices, along with the development of clouds and cloudlet technology, there has been an increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage, data sharing, etc. The traditional healthcare system often requires the delivery of medical data to the cloud, which involves users’ sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. This issue motivates construction to maintain the security and privacy of healthcare data by applying the neural network approach. During this process, the system examines the various intermediate attacks; malwares for detecting the threats also eliminate the unauthorized access in the cloud storage-based healthcare system. In this paper, A termite colony optimization deep belief neural network (TDBNN) is proposed for the feature selection process with the combination of Levenberg-Marquardt for the back-propagation stage, which hence detects the list of attacks that occurred in the cloud and categorizes it. The proposed TDBNN is compared with two well-known, state-of-the-art methods, the recurrent neural network oppositional crow search algorithm and voting extreme learning machine, in terms of precision, sensitivity, specificity, f1-score, and kappa score. The proposed method achieves 99.03 % accuracy, 99.03 % precision, 99.03 % sensitivity, 99.76 % specificity, a 99.03 % f1-score, and a 98.79 % of kappa score.

Integration of Mobile Computing and Cloud Computing in Healthcare

Integration of Mobile Computing and Cloud Computing in Healthcare

Key Challenges and Opportunities for Cloud Technology in Health Care: Semistructured Interview Study.

BackgroundThe use of cloud computing (involving storage and processing of data on the internet) in health care has increasingly been highlighted as having great potential in facilitating data-driven innovations. Although some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind.ObjectiveWe aim to explore the existing challenges and barriers to the use of cloud computing in health care settings and investigate how perceived risks can be addressed.MethodsWe conducted a qualitative case study of cloud computing in health care settings, interviewing a range of individuals with perspectives on supply, implementation, adoption, and integration of cloud technology. Data were collected through a series of in-depth semistructured interviews exploring current applications, implementation approaches, challenges encountered, and visions for the future. The interviews were transcribed and thematically analyzed using NVivo 12 (QSR International). We coded the data based on a sociotechnical coding framework developed in related work.ResultsWe interviewed 23 individuals between September 2020 and November 2020, including professionals working across major cloud providers, health care provider organizations, innovators, small and medium-sized software vendors, and academic institutions. The participants were united by a common vision of a cloud-enabled ecosystem of applications and by drivers surrounding data-driven innovation. The identified barriers to progress included the cost of data migration and skill gaps to implement cloud technologies within provider organizations, the cultural shift required to move to externally hosted services, a lack of user pull as many benefits were not visible to those providing frontline care, and a lack of interoperability standards and central regulations.ConclusionsImplementations need to be viewed as a digitally enabled transformation of services, driven by skill development, organizational change management, and user engagement, to facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment.

Review of Mobile Cloud Computing in Healthcare for Diabetics Patients Using Machine Learning Techniques

Abstract: The number of chronic diseases such as diabetes, cancer, heart disease, and others is fast increasing in our daily lives. The disadvantages of the traditional healthcare system are becoming more prevalent. One of the most important is that healthcare is only offered in hospitals. No one has access to it and no one is monitoring it. Patients' information is securely acquired from the hospital, with their consent, and monitored on a regular basis using their smart phones in mobile cloud computing. On a daily basis, a real-time mobile cloud health monitoring system is used. The patient's specifics concerning various metrics for data collection, such as blood glucose level, high/low blood pressure, high cholesterol, oxygen level, and so on, are being monitored. Diabetic patients are tracked via mobile cloud-IoT and certain wearable health tracking devices and sensors. Doctors will review the individuals' medical records and make recommendations for improving their health. In the future, it will aid in the control or recovery of diabetics. To provide improved security and performance, the proposed system can leverage advanced encryption techniques in conjunction with a machine learning classifier. Keyword: Predictive analytics; Prediction models; Machine learning; Classifications, Healthcare, Diabetes, Blood Glucose and privacy module.

IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes

Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose level in the blood and alert user to the type-1 Diabetes class once a certain critical level is surpassed. This can lead the body of the patient to work at critical levels until the medicine is taken in order to reduce the glucose level, consequently increasing the risk of causing considerable health damages in case of the intake is delayed. To overcome the latter, a new approach based on cutting-edge software and hardware technologies is proposed in this paper. Specifically, an artificial intelligence deep learning (DL) model is proposed to predict glucose levels in 30 min horizons. Moreover, Cloud computing and IoT technologies are considered to implement the prediction model and combine it with the existing wearable CGM model to provide the patients with the prediction of future glucose levels. Among the many DL methods in the state-of-the-art (SoTA) have been considered a cascaded RNN-RBM DL model based on both recurrent neural networks (RNNs) and restricted Boltzmann machines (RBM) due to their superior properties regarding improved prediction accuracy. From the conducted experimental results, it has been shown that the proposed Cloud&DL-based wearable approach achieves an average accuracy value of 15.589 in terms of RMSE, then outperforms similar existing blood glucose prediction methods in the SoTA.

Cloud Computing in Healthcare - Investigation of Threats, Vulnerabilities, Future Challenges and Counter Measure

Cloud Computing in Healthcare - Investigation of Threats, Vulnerabilities, Future Challenges and Counter Measure

Security and Privacy Requirements for Cloud Computing in Healthcare

Cloud computing promises essential improvements in healthcare delivery performance. However, its wide adoption in healthcare is yet to be seen, one main reason being patients’ concerns for security and privacy of their sensitive medical records. These concerns can be addressed through corresponding security and privacy requirements within the system engineering process. Despite a plethora of related research, security and privacy requirements for cloud systems and services have seldomly been investigated methodically so far, whereas their individual priorities to increase the system success probability have been neglected. Against this background, this study applies a systematic requirements engineering process: First, based on a systematic literature review, an extensive initial set of security and privacy requirements is elicited. Second, an online survey based on the best-worst scaling method is designed, conducted, and evaluated to determine priorities of security and privacy requirements. Our results show that confidentiality and integrity of medical data are ranked at the top of the hierarchy of prioritized requirements, followed by control of data use and modification, patients’ anonymity, and patients’ control of access rights. Availability, fine-grained access control, revocation of access rights, flexible access, clinicians’ anonymity, as well as usability, scalability, and efficiency of the system complete the ranking. The level of agreement among patients is rather small, but statistically significant at the 0.01 level. The main contribution of the present research comprises the study method and results highlighting the role of strong security and privacy and excluding any trade-offs with system usability. Enabling a richer understanding of patients’ security and privacy requirements for adopting cloud computing in healthcare, these are of particular importance to researchers and practitioners interested in supporting the process of security and privacy engineering for health-cloud solutions. It further represents a supplement that can support time-intensive negotiation meetings between the requirements engineers and patients.

Cloud Computing in Healthcare - A vision, challenges and future directions

In the era of cloud computing (CC), many organisations have successfully adopted and leveraged CC capabilities to improve their business performance and economic performance. However, past literature on CC has put the limited focus on cloud-healthcare domain. In this context, this paper aims to provide a systematic literature review of cloud computing in healthcare (CCH) capabilities, usages, and other challenges across countries over a period of the healthcare industry. The paper presents the bibliometric analysis of research papers of 750 found on CC from 2011 to 2017 and after scrutinising 81 articles were considered for the review, which was related to CCH. This article contributes to theorising CC capabilities in the context of healthcare and provides future direction of research in this field.

Application of Cloud Computing In Healthcare A Review

Application of Cloud Computing In Healthcare A Review

Bigdata Driven Cloud Security: A Survey

Cloud Computing (CC) is a fast-growing technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Recently, it has been observed that massive growth in the scale of data or big data generated through cloud computing. CC consists of a front-end, includes the users’ computers and software required to access the cloud network, and back-end consists of various computers, servers and database systems that create the cloud. In SaaS (Software as-a-Service – end users to utilize outsourced software), PaaS (Platform as-a-Service-platform is provided) and IaaS (Infrastructure as-a-Service-physical environment is outsourced), and DaaS (Database as-a-Service-data can be housed within a cloud), where leading / traditional cloud ecosystem delivers the cloud services become a powerful and popular architecture. Many challenges and issues are in security or threats, most vital barrier for cloud computing environment. The main barrier to the adoption of CC in health care relates to Data security. When placing and transmitting data using public networks, cyber attacks in any form are anticipated in CC. Hence, cloud service users need to understand the risk of data breaches and adoption of service delivery model during deployment. This survey deeply covers the CC security issues (covering Data Security in Health care) so as to researchers can develop the robust security application models using Big Data (BD) on CC (can be created / deployed easily). Since, BD evaluation is driven by fast-growing cloud-based applications developed using virtualized technologies. In this purview, MapReduce [12] is a good example of big data processing in a cloud environment, and a model for Cloud providers.

Design and Implementation of Security in Healthcare Cloud Computing

As technology keeps on evolving, different organisations make use of the recent trends in technology and the health sector is no exception. As the cost of healthcare services is increasing, healthcare professionals are becoming scarce. Healthcare organisations have also adopted the latest technology of cloud computing. The introduction of cloud computing has proved to be a feasible idea on the information technology community. Rather than keeping the patient’s information in a file in a health facility he/she was treated in, the information is stored in a cloud so that it can be shared amongst all health organisations and health professionals. Information is stored in a central place where it can be easily accessed, thus saving time and avoiding repetition of always writing the information each time a patient is attended to in a different facility. However, there are issues with sharing such information on the cloud since it is sensitive information. Ensuring this sensitive information security, availability and scalability are a major factor in the cloud computing environment. In this study, we proposed a mathematical model for measuring the availability of data and machines (nodes). We also present the current state-of-the-art research in this field by focusing on several shortcomings of current healthcare solutions and standards and we further proposed a system that will encrypt data before it is being sent to the cloud. The system is intended to be linked to the cloud in such a way that, before the client submits the data to the cloud and, the data will go through that system for encryption. The paper presents the steps to achieve the proposed system and also a sample encrypted and decrypted file.

Security Challenges in Healthcare Cloud Computing: A Systematic Review

<p><strong>BACKGROUND:</strong> Healthcare data are very sensitive records that should not be made available to unauthorized people in order for protecting patient's information security. However, in progressed technologies as cloud computing which are vulnerable to cyber gaps that pose an adverse impact on the security and privacy of patients’ electronic health records and in these situations, security challenges of the wireless networks need to be carefully understood and considered. Recently, security concerns in cloud computing environment are a matter of challenge with rising importance.</p><p><strong>OBJECTIVE:</strong> In this study a systematic review to investigate the security challenges in cloud computing was carried out. We focused mainly on healthcare cloud computing security with an organized review of 210 full text articles published between 2000 and 2015.</p><p><strong>METHOD:</strong> A systematic literature review was conducted including PubMed, Science direct, Embase, ProQuest, Web of science, Cochrane, Emerald, and Scopus databases.</p><p><strong>FINDINGS:</strong> Using the strategies described, 666 references retrieved (for research question one 365, research question two 201, and research question three 100 references).</p><p><strong>IMPROVEMENTS:</strong> Review of articles showed that for ensuring healthcare data security, it is important to provide authentication, authorization and access control within cloud's virtualized network. Issues such as identity management and access control, Internet-based access, authentication and authorization and cybercriminals are major concerns in healthcare cloud computing. To manage these issues<strong> </strong>many involved events such as Hybrid Execution Model, VCC-SSF, sHype Hypervisor Security Architecture, Identity Management, and Resource Isolation approaches<em> </em>have to be defined for using cloud computing threat management processes.</p>

Cloud Computing in Healthcare: A Space of Opportunities and Challenges.

Cloud Computing in Healthcare: A Space of Opportunities and Challenges.

Introduction to the Special Issue on Cloud for Health

It is a pleasure to present this special issue covering subjects related to applying cloud computing to bioinformatics, biomedicine, and health, including solutions to area problems and architectural adaptation of cloud systems to fit those problems. The special issue includes papers selected from the workshops C4Bio 2014 and CCGrid-Health 2014 that, after further extension and additional review, were selected for publication. C4Bio 2014 and CCGrid-Health 2014 were held in Chicago, during May 26-29th 2014 within the framework of the 14th IEEE/ACM International Symposium on Cluster, and Grid Computing (CCGrid 2014). The special issue begins with the review paper Cloud Computing in Healthcare and Biomedicine. Calabrese and Cannataro explore the current academic and commercial cloud-based solutions developed in the healthcare and biomedicine domains, designed in order to satisfy the specific needs (e.g. massive scalable computing and storage, data sharing, on-demand access anytime and anywhere) in these fields. Subsequently, the paper summarizes main issues regarding the use of Computing in healthcare and biomedicine. The article Selected Approaches and Frameworks to Carry out Genomic Data Analysis on the Cloud, by Church and Goscinski, examines commonly used cloud-based genomic analysis services, introduces the approach of exposing data as services and proposes two new solutions, namely HPCaaS and Uncinus, which aim to automate service development, deployment process and data provision. By comparing and contrasting these solutions, the authors identify key mechanisms of service creation, execution and data access required to support non-computing specialists employing clouds. Ahmad, Hasan, and Siddique propose to formally reason about molecular pathways of Zsyntax based deduction using the HOL4 theorem prover and developing a biologist friendly graphical user interface. On the Formalization of Zsyntax with Applications in Molecular Biology shows additionally a use case with the formal analysis of cancer-related molecular pathway, i.e., TP53 degradation and metabolic pathway, known as Glycolysis. The article Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partiotioning and Workflow Fusion, by Choudhury et al., discusses different strategies for alleviating the computational constraints experienced by several data-intensive bioinformatics applications, presenting a workflow-based framework to parallelize the compute-intensive tasks of genome alignment and variant calling, the two most important stages in most comparative genomics applications. This framework provides workflow fusion to merge multiple sequential workflows into a single optimized workflow that benefits from caching, eliminating choke points, and stacked partitioning. In Performance Comparison and Tuning of Virtual Machines For Sequence Alignment Software, Estrada et al. explore the performance cost of virtualization for the fast growing application domain of genomics to inform the feasibility of running an NGS pipeline in a cloud, and in doing so consider two prevalent short-read sequence alignment programs, BWA and Novoalign. They execute these applications in three separate open-source system virtualization solutions (KVM hypervisor, Xen paravirtualized hypervisor, and Linux Containers) and compare the runtime in each environment against the runtime of the same system without virtualization and measure the relative performance of each hypervisor, presenting tuning suggestions for cloud implementors and users. The article Extending XNAT towards a Cloud-based Quality Assessment Platform for Retinal Optical Coherence Tomographies, by Jansen et al., presents a platform designed for automatic quality assessment of retinal OCTs and provided as a cloud-based service employing OpenStack. It extends the image management platform XNAT by services to calculate and store quality measures. It is also extensible regarding new quality measure algorithms, allowing the developer to upload, compile and test code for the system's architecture. Finally, the paper A Tool for Managing the X1.V1 Platform on the Cloud, by Marzini et al., proposes a framework for managing the execution of the X1.V1 platform on the Cloud. This framework enables an easy, quick, and secure management of the resources allocation and reallocation to X1.V1 Virtual Machines, in order to enhance the platform performance, optimize resource utilization and, consequently, reduce the whole services cost. We would like to thank all the reviewers for their effort, cooperation, and valuable feedback and all the authors who submitted papers to C4Bio 2014 and CCGrid-Health 2014 and to this Special Issue.

Cloud Computing in Healthcare and Biomedicine

High throughput platforms available in clinical settings or in research laboratories, such as magnetic resonance imaging, microarray, mass spectrometry and next-generation sequencing, are producing an increasing volume of clinical and omics data that poses new issues in terms of secure data storage, models for data integration and analysis, and high performance computing. Cloud Computing offers large scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and it supports easy but powerful distributed computing models, for facing those issues. In fact, in the recent years it has been adopted for the deployment of several applications in healthcare and bioinformatics both in academia and in the industry. However, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This paper reviews main cloud-based healthcare and biomedicine applications; with a special focus on healthcare, biomedicine and bioinformatics solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

Opportunities and Threats of Cloud Computing in HealthCare

With its on demand services model, cloud computing is making its way in every field and healthcare is no exception to it. Healthcare is readily moving its way to adaptation of cloud computing. This shift is to enjoy clouds services of minimized cost, effective use of resources and maximized availability of services. However, like other fields healthcare is hesitant to embrace the cloud computing environment due to concerns of data privacy, availability and data integrity. This paper provides a brief overview of opportunities and threats of cloud computing (CC) in healthcare industry. The major aim of this review paper is to find out the current and future opportunities and threats faced by the cloud services providers in healthcare.

A Survey of the State of Cloud Computing in Healthcare

Analysts, researchers and organizations alike seem to agree that cloud computing will be a defining trend in the coming decade impacting wide range of businesses and how those businesses are practiced. Large technology companies are already investing millions of dollars in building infrastructure, services, tools and applications to facilitate cloud computing for consumers, organizations and businesses to use and take advantage. It remains to be seen how cloud computing will impact the healthcare business since it is very diverse, complex and unique and presents several challenges such as protecting members health records in addition to following HIPAA guidelines set by federal compliance regulations. In addition to these the rising cost of healthcare solutions is another major concern. Efforts are being made to reduce these costs for consumers and IT will play a big role in achieving it and also improving clinical and quality outcomes for patients. It will be very interesting to see how cloud computing will address and contribute towards these issues in the healthcare industry. The purpose of this paper is to explore the current state and trends of cloud computing in healthcare.

What's the Forecast for Cloud Computing in Healthcare?

A cloud front has moved in—cloud computing that is! Many advantages and efficiencies make cloud computing networks attractive. Next to the Internet, cloud computing may very well be the biggest information technology (IT) systems game-changer. Most large IT system providers are heavily invested in preparing for computing that’s 100% web. All computing is done via the browser. Clouds are an attractive network infrastructure alternative due to overall reduced IT costs, faster deployment times, increased efficiencies, and increases in flexibility and choice. Cool things begin to happen. For example, with reduced infrastructure and capital equipment costs, smaller institutions may find it easier to afford electronic health record (EHR) systems or apply more of the budget to staff or other operational expenditure needs. Cloud computing refers to computing in which information, services, and resources are provided as needed over a network (the “cloud”). What’s more, the on-demand resources can be available with little system management effort or service provider interaction. Just as rapidly, they can be decommissioned when no longer needed. Think of it: Any application in the cloud is available to you on demand with no installation process or need to back it up. The cloud also manages any required software licensing. In this way, a sudden need for added server space can be met immediately (aka “server space elasticity”). The cloud can provide new applications as well, for example, the coediting of a document at the same time, with one person working on an iPad and another on a PC.