cloud-based
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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 infusion of information communication technology (ICT) into health services is emerging as an active area of research. It has several advantages but perhaps the most important one is providing medical benefits to one and all irrespective of geographic boundaries in a cost effective manner, providing global expertise and holistic services, in a time bound manner. This paper provides a systematic review of technological growth in eHealth services. The present study reviews and analyzes the role of four important technologies, namely, satellite, internet, mobile, and cloud for providing health services.
- MeSH
- analýza nákladů a výnosů MeSH
- cloud computing MeSH
- dostupnost zdravotnických služeb MeSH
- internet MeSH
- lékařská informatika trendy MeSH
- lidé MeSH
- mobilní telefon MeSH
- počítače do ruky MeSH
- poskytování zdravotní péče MeSH
- přístup k informacím MeSH
- satelitní přenosy MeSH
- sociální média MeSH
- telemedicína trendy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy 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
Method of Monte Carlo simulation of gamma radiation fields in the vicinity of the cloud of air contaminated by the radionuclides from emergency leakage from nuclear power plant was designed and tested. Air kerma rates distributions as well as gamma field spectral distributions were calculated for the Gaussian cloud model, different atmospherical conditions and emergency scenarios source terms. Based on this model, the radiation doses in the aerial vehicle (helicopter) and its shielding properties in the radiation fields in cloud vicinity were evaluated with an aim to prepare a method providing data for planning adequate radiation protection of the personnel during airborne monitoring/interventions.
- MeSH
- dávka záření MeSH
- havárie elektrárny Fukušima MeSH
- jaderné elektrárny * MeSH
- lidé MeSH
- metoda Monte Carlo MeSH
- monitorování radiace metody MeSH
- počítačová simulace * MeSH
- radioaktivní látky znečišťující vzduch analýza MeSH
- vystavení vlivu životního prostředí analýza MeSH
- záření gama MeSH
- Check Tag
- lidé MeSH
- 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
Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.
- MeSH
- algoritmy MeSH
- datové soubory jako téma * MeSH
- robotika MeSH
- Publikační typ
- časopisecké články MeSH
The interest in the determination of different Sb species in natural waters is due to the fact that their toxicological and physiological behavior strongly depends on their chemical forms and oxidation states. The purpose of this article is to review and evaluate methods for Sb speciation in waters based on selective hydride generation of Sb (III) and on coupling of different separation techniques (liquid-liquid extraction, solid phase extraction, cloud point extraction, ion-exchange HPLC and GC) with atomic spectrometric methods (AAS, atomic emission spectrometry and atomic fluorescence spectrometry). This review covers the literature published over the period 1998-2006.
Národní lékařská knihovna vyvíjí a provozuje portál Medvik (www.medvik.cz), který zajišťuje uživatelům přístup k bibliografickým a autoritním databázím provozovaným v systému MEDVIK (Medicínská virtuální knihovna). Řešení předchozí verze portálu Medvik nebylo optimální pro rychlé a uživatelsky přívětivé vyhledávání odpovídající očekáváním uživatelů ve 21. století. Bylo proto vytvořeno nové řešení, které automaticky agreguje záznamy z uvedených bází do jedné společné databáze a umožňuje plnotextově prohledávat všechny báze z jednoho místa. Nový portál využívá tezauru Medical Subject Headings ve formě seznamu vážených hesel („tag cloud“) založený na počtu jejich výskytu v celé databázi nebo v bibliografických záznamech výsledku dotazu s možnostmi fasetové navigace. Uživatelé mohou zužovat nebo rozšiřovat výsledky hledání pomocí dynamicky generovaného seznamu hesel MeSH. Uživatelům je k dispozici řada funkcí umožňujících rychlý a pohodlný přístup k poskytovaným informačním zdrojům a navazujícím službám.
National Medical Library develops and operates the Portal Medvik (www.medvik.cz), which provides users with access to bibliographic databases and authority files maintained in the MEDVIK system (Virtual Medical Library). The design of the previous version of the portal was not optimal for fast and user-friendly search corresponding expectations of users in the 21st century. It was therefore created a new solution that automatically aggregates the records from these databases into one common database and provides full-text search from one place. The new portal uses Medical Subject Headings in the form of a tag cloud - a list of weighted MeSH headings - based on the number of their occurrence in the whole database or in the bibliographic records retrieved in a query results with the possibilities of facet navigation. Users can narrow or expand query results using dynamically generated list of MeSH headings. There are many features for users that enable quick and convenient access to information sources and provided follow-up services.
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
- MeSH
- Bayesova věta MeSH
- big data MeSH
- časná diagnóza MeSH
- internet věcí * MeSH
- Publikační typ
- časopisecké články MeSH
Objectives: The paper presents a framework and tools for developing models useful for implementing complex Information Systems. It presents a case study for an Obstetrics- Gynecology Department and connected departments. The advantages of using models for creating complex OGD Information Systems together with a standardized communication will lead to an advanced interoperability and also will have benefits in patient care and in time will reduce the medical errors. Methods: This paper presents the modeling process using the Generic Component Model (GCM) in four steps. The real OGD system is described based on the five RMODP (Reference Model of Open Distributed Processing) views. The paper presents the Obstetrics-Gynecology Department model based on the real workflow using Business Process Modeling and Notation and a specialized software - Bizagy. Communication between OGD and other medical units is based on HL7 Clinical Document Architecture CDA. The Obstetrics-Gynecology Department Information System (OGD IS) is developed based on the model, in Visual Studio.NET 2010, using ASP.NET pages and C] language, and Microsoft SQL Server 2008. Results: The paper presents a model represented with Business Process Modeling and Notation and its possibilities to offer support for software developers to create flexible and portable information systems. Based on the workflow in the OGD, including the communication between OGD and other medical units, was developed a model and consequently the OGD IS. Conclusions: For the future, the OGD IS will be extended with new functionalities: possibilities to introduce medication related to a Database in the cloud to receive suggested treatments. The advantages of using the OGD IS are reflected in a better patient care, and the treatments will be more documented which will determine less medical errors.
