cloud computing use
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Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.
Currently, Biomedicine is characterised by a growing need for processing large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides a lot of advantages such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing. Firstly, the forms of cloud computing are described. Secondly, the potential benefits and limitations of Biomedicine technology are discussed. Finally, the current (present) situation of using this technology in Biomedicine in the Czech Republic from an economic point of view is analysed.
- MeSH
- biomedicínské technologie * MeSH
- informační systémy * MeSH
- internet MeSH
- počítačové systémy MeSH
- poskytování zdravotní péče * metody MeSH
- rozšiřování inovací MeSH
- software MeSH
- ukládání a vyhledávání informací MeSH
- využití lékařské informatiky MeSH
- Publikační typ
- práce podpořená grantem MeSH
- přehledy MeSH
Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients' biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely.
- MeSH
- algoritmy MeSH
- ambulantní monitorování přístrojové vybavení MeSH
- biometrie MeSH
- chorobopisy MeSH
- cloud computing * MeSH
- důvěrnost informací MeSH
- elektronické zdravotní záznamy MeSH
- internet MeSH
- lékařská informatika přístrojové vybavení MeSH
- lidé MeSH
- poskytování zdravotní péče MeSH
- programovací jazyk MeSH
- sběr dat MeSH
- ukládání a vyhledávání informací metody MeSH
- zabezpečení počítačových systémů * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The academic de.NBI Cloud offers compute resources for life science research in Germany. At the beginning of 2017, de.NBI Cloud started to implement a federated cloud consisting of five compute centers, with the aim of acting as one resource to their users. A federated cloud introduces multiple challenges, such as a central access and project management point, a unified account across all cloud sites and an interchangeable project setup across the federation. In order to implement the federation concept, de.NBI Cloud integrated with the ELIXIR authentication and authorization infrastructure system (ELIXIR AAI) and in particular Perun, the identity and access management system of ELIXIR. The integration solves the mentioned challenges and represents a backbone, connecting five compute centers which are based on OpenStack and a web portal for accessing the federation.This article explains the steps taken and software components implemented for setting up a federated cloud based on the collaboration between de.NBI Cloud and ELIXIR AAI. Furthermore, the setup and components that are described are generic and can therefore be used for other upcoming or existing federated OpenStack clouds in Europe.
- MeSH
- biologické vědy * MeSH
- software * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Německo MeSH
Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.
- Publikační typ
- časopisecké články MeSH
COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.
- MeSH
- algoritmy MeSH
- COVID-19 * MeSH
- internet věcí * MeSH
- lidé MeSH
- poskytování zdravotní péče MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.
Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic markers ranging from benign accumulations to signs of severe pathologies. The falx cerebri, a dural fold that separates the cerebral hemispheres, presents challenges in visualization due to its low contrast in standard imaging techniques. Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, have significantly transformed radiological diagnostics. This study aims to explore the application of AI in the segmentation and detection of falx cerebri calcifications using Cone-Beam Computed Tomography (CBCT) images through a comprehensive literature review and a detailed case report. The case report presents a 59-year-old patient diagnosed with falx cerebri calcifications whose CBCT images were analyzed using a cloud-based AI platform, demonstrating effectiveness in segmenting these calcifications, although challenges persist in distinguishing these from other cranial structures. A specific search strategy was employed to search electronic databases, yielding four studies exploring AI-based segmentation of the falx cerebri. The review detailed various AI models and their accuracy across different imaging modalities in identifying and segmenting falx cerebri calcifications, also highlighting the gap in publications in this area. In conclusion, further research is needed to improve AI-driven methods for accurately identifying and measuring intracranial calcifications. Advancing AI applications in radiology, particularly for detecting falx cerebri calcifications, could significantly enhance diagnostic precision, support disease monitoring, and inform treatment planning.
- MeSH
- dura mater diagnostické zobrazování MeSH
- kalcinóza * diagnostické zobrazování MeSH
- lidé středního věku MeSH
- lidé MeSH
- počítačová tomografie s kuželovým svazkem * metody MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- kazuistiky MeSH
A common Authentication and Authorisation Infrastructure (AAI) that would allow single sign-on to services has been identified as a key enabler for European bioinformatics. ELIXIR AAI is an ELIXIR service portfolio for authenticating researchers to ELIXIR services and assisting these services on user privileges during research usage. It relieves the scientific service providers from managing the user identities and authorisation themselves, enables the researcher to have a single set of credentials to all ELIXIR services and supports meeting the requirements imposed by the data protection laws. ELIXIR AAI was launched in late 2016 and is part of the ELIXIR Compute platform portfolio. By the end of 2017 the number of users reached 1000, while the number of relying scientific services was 36. This paper presents the requirements and design of the ELIXIR AAI and the policies related to its use, and how it can be used for serving some example services, such as document management, social media, data discovery, human data access, cloud compute and training services.
- MeSH
- biomedicínský výzkum metody MeSH
- lidé MeSH
- software * MeSH
- systémy řízení databází * MeSH
- uživatelské rozhraní počítače MeSH
- výpočetní biologie metody MeSH
- výzkumní pracovníci MeSH
- zabezpečení počítačových systémů * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Vyšší kvalita a nižší náklady na péči o pacienta závisejí na digitalizaci zdravotních záznamů a na přechodu na elektronické záznamy o pacientech. Tyto záznamy představují typ citlivých informací označovaných také jako elektronické chráněné zdravotní záznamy (electronic Protected Health Information–ePHR). Ve srovnání se svými papírovými ekvivalenty představují citlivé informace v elektronické podobě nová slabá místa. Bezpečnostní rizika a rizika spojená s ochranou citlivých informací se vlivem řady sílících trendů ve zdravotnictví zvyšují. K takovým rizikům patří např. mobilita lékařů a využívání bezdrátových sítí, výměna zdravotnických informací, cloudové aplikace, přístup typu „přineste si vlastní počítač“ či používání osobních zdravotních záznamů (Personal Health Records–PHRs). Rafinovanost malwaru a bezpečnostní hrozby se neustále stupňují. K těmto problémům lze dále přiřadit omezené finanční prostředky, které mají zdravotnická zařízení k dispozici v oblasti snižování rizik, a zároveň zvyšující se závažnost důsledků případného selhání při ochraně citlivých informací. Tato bílá kniha popisuje standardní postup, pomocí něhož mohou zdravotnická zařízení vyhodnotit rizika a zformulovat nutné požadavky na zabezpečení a ochranu dat. Nabízíme také několikavrstvou strategii hloubkové ochrany, která může zdravotnickým organizacím pomoci v průběhu životního cyklu hrozby snížit rizika a zabezpečit tak důvěrnost, celistvost a dostupnost citlivých informací. Na základě toho pak hovoříme o specifických bezpečnostních potřebách a potřebách ochrany dat ve zdravotnických organizacích a popisujeme několik technologií Intel®, jež mohou těmto potřebám vyjít vstříc, protože snižují riziko ztráty nebo odcizení citlivých informací chrání momentálně nevyužívané, přesouvané i právě používané citlivé informace chrání přístup k citlivým informacím pomocí silné autentizace umožňují lépe plnit kritéria politiky zabezpečení a ochrany osobních dat.
Higher quality and lower costs of patient care depend on digitization of medical records and on switching to electronic patient records. These records contain a type of sensitive information also called electronic protected health information – ePHR. Compared to its paper equivalents, sensitive information in the electronic form is associated with new weak points. Security risks and risks associated with the protection of sensitive information have been rising due to a number of trends growing ever stronger in medicine. Such risks include, for example, mobility of the physicians and the use of wireless networks, medical data exchange, cloud applications, “bring your own computer“ access or the use of personal health records – PHRs. The artfulness of malware and the security threats have been rising constantly. Moreover, restricted financial resources available to medical facilities in the field of risk reduction can be added to such problems, as well as ever more serious consequences of any failure of sensitive information protection. This white book describes a standard procedure that can be used by medical facilities to assess the risks and formulate necessary requirements for data security and protection. Multiple layered strategy of deep protection is also offered, which may help medical organizations to reduce the risks in the course of the threat life cycle, and thus to secure the confidence, integrity and availability of sensitive information. Based on the above, specific security needs and data protection needs in medical organizations are discussed, and several Intel® technologies are described, which may accommodate the needs, given that they reduce the risk of loss or theft of sensitive information, they protect sensitive information currently being unused, transferred and used, and they protect access to sensitive information using strong authentication that makes it possible to better fulfil the criteria of the security and personal data protection policy.
