کارابرن عزیز، مقالات isi بالاترین کیفیت ترجمه را دارند، ترجمه آنها کامل و دقیق می باشد (محتوای جداول و شکل های نیز ترجمه شده اند) و از بهترین مجلات isi انتخاب گردیده اند. همچنین تمامی ترجمه ها دارای ضمانت کیفیت بوده و در صورت عدم رضایت کاربر مبلغ عینا عودت داده خواهد شد.
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Interactive visualization and analysis of antihypertensive prescriptions using National Health Insurance claims data
بصری سازی تعاملی و تحلیل نسخه های ضد فشار خون با استفاده از ادعاهای بیمه ملی بهداشت و درمان-2018
Interactive visualization is an important approach to help to understand and to explain large amounts of data, particularly in light of decision support. Although data visualization have been introduced in healthcare and clinical fields, analytics has often been performed by data experts, focused on specific subjects, or insufficient statistical evidence. Therefore, this study suggests the procedures of effective and efficient visualization of big data for general healthcare researchers. Specifically, the procedure includes conventional regression analyses followed by interactive data visualization for prescription patterns of antihypertensive drugs. Methods: As a large-scale nationally representative prescription data, the Korean National Health Insurance claims data were collected. Conventional descriptive and regression analyses were conducted for therapy decision and prescription patterns using the software R. Then, based on the statistically significant findings, dashboards were developed to visualize interactively the patterns of prescriptions using the software Tableau. Results: Major characteristics (genders, age groups, healthcare institutions, and comorbidities) explained the differences in therapy and the average number of drugs prescribed as well as differences among most commonly prescribed drug classes. Two interactive dashboards were created for visualizing prescription patterns with incorporation of horizontal bar charts, packed bubble charts, treemaps, filled maps, radar charts, box and whisker plots, and filters. Conclusion: In the current big data era, interactive data visualization offers substantial opportunities to have comprehensive view, extract insights and evidence from the flood of vast amounts of data. This study’s interactive visualizations can provide healthcare professionals insight into prescription patterns and demonstrate the value of creating interactive dashboards to support informed and timely decision-making. Exploring big data using interactive visualization is expected to deliver many future benefits in healthcare fields.
Keywords: Prescriptions; National Health Insurance Claims database; Hypertension; Interactive Visualization
A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
معماری جدید اینترنت اشیاء و اکوسیستم داده های بزرگ برای نظارت بر سیستم مراقبت سلامت هوشمند و سیستم هشدار دهنده امن-2018
Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.
Keywords: Wireless sensor networks ، Internet of Things ، Big data analytics ، Cloud computing and health car
Big data for internet of things: A survey
داده های بزرگ برای اینترنت اشیا: یک مرور-2018
With the rapid development of the Internet of Things (IoT), Big Data technolo gies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is ex tensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data ap proaches in each domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.
Keywords: Big Data, data analytics, Internet of Things, healthcare, energy, transportation, building automation, Smart Cities
Improving the Use of Big Data Analytics within Electronic Health Records: A Case Study based OpenEHR
بهبود استفاده از تجزیه و تحلیل داده های بزرگ در پرونده های بهداشت الکترونیکی: یک مطالعه موردی مبتنی بر OpenEHR-2018
Recently there has been an increasing adoption of electronic health records (EHRs) in different countries. Thanks to these systems, multiple health bodies can now store, manage and process their data effectively. However, the existence of such powerful and meticulous entities raise new challenges and issues for health practitioners. In fact, while the main objective of EHRs is to gain actionable big data insights from the health workflow, very few physicians exploit widely analytic tools, this is mainly due to the fact of having to deal with multiple systems and steps, which completely discourage them from engaging more and more. In this paper, we shed light and explore precisely the proper adaptation of analytical tools to EHRs in order to upgrade their use by health practitioners. For that, we present a case study of the implementation process of an EHR based OpenEHR and investigate health analytics adoption in each step of the methodology.
Keywords: Electronic Health Records; EHRs; Analytic tools; Big Data; Health Practitioners
Leveraging hospital big data to monitor flu epidemics
استفاده از داده های بزرگ بیمارستان برای کنترل اپیدمی های آنفولانزا-2018
Background and Objective: Influenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics. Methods: We used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity. Results: We found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson’s correla tion coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014–15. This suggests that both ICD-10 codes and PCR re sults are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients’ characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network. Conclusions: Conclusions: Hospital big data seem to have a great potential for monitoring influenza epi demics in near real-time. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes. Ad ditional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.
Keywords: Health big data ، Clinical data warehouse ، Information retrieval system ، Health Information Systems ، Influenza ، Sentinel surveillance
Fall detection system for elderly people using IoT and Big Data
سیستم تشخیص سقوط برای سالمندان با استفاده از اینترنت اشیا و داده های بزرگ-2018
Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elder and a significant decrease in his mobility, independence and life quality. In that sense, the present work proposes an innovative IoT-based system for detecting falls of elderly people in indoor environments, which takes advantages of low-power wireless sensor networks, smart devices, big data and cloud computing. For this purpose, a 3D-axis accelerometer embedded into a 6LowPAN device wearable is used, which is responsible for collecting data from movements of elderly people in real-time. To provide high efficiency in fall detection, the sensor readings are processed and analyzed using a decision trees based Big Data model running on a Smart IoT Gateway. If a fall is detected, an alert is activated and the system reacts automatically by sending notifications to the groups responsible for the care of the elderly people. Finally, the system provides services built on cloud. From medical perspective, there is a storage service that enables healthcare professional to access to falls data for perform further analysis. On the other hand, the system provides a service leveraging this data to create a new machine learning model each time a fall is detected. The results of experiments have shown high success rates in fall detection in terms of accuracy, precision and gain.
Keywords: Fall detection; Internet-of-Things; Big Data, 6LowPAN; wearable sensor; Smart IoT Gateway; fall detection; decision tree learning algorithm; accelerometer; elderly people.
A survey towards an integration of big data analytics to big insights for value-creation
مروری به سوی تجمیع تحلیل داده های بزرگ به بینشی بزرگ برای ایجاد ارزش-2018
Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an en ormous amount of data and provides a new opportunity that is helpful in relevant decision making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven Vs characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and tech nologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future di rections of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-crea tion; and (iii) the current challenges of Big Data Analytics as well as possible future directions.
Keywords: Big data ، Data analytics ، Machine learning ، Big data visualization ، Decision-making ، Smart agriculture ، Smart city application ، Value- reation ، Value-discover ، Value-realization
An integrated big data analytics-enabled transformation model: Application to health care
مدل تبدیل تحلیلی داده های بزرگ مجتمع: کاربرد در مراقبت های بهداشتی-2018
A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data analytics capabilities, IT-enabled transformation practices, benefit dimensions, and business values. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.
Keywords: Big data analytics ، IT-enabled transformation ، Practice-based view ، Business value of IT ، Healthcare ، Content analysis
Using Big Data in oncology to prospectively impact clinical patient care: A proof of concept study
استفاده از داده های بزرگ در انکولوژی برای تاثیر فزاینده مراقبت های بالینی بیمار: اثبات مفهوم مطالعه-2018
Objective: Big Data is widely seen as a major opportunity for progress in the practice of personalized medicine, attracting the attention from medical societies and presidential teams alike as it offers a unique opportunity to enlarge the base of evidence, especially for older patients underrepresented in clinical trials. This study prospec tively assessed the real-time availability of clinical cases in the Health & Research Informatics Total Cancer Care™ (TCC) database matching community patients with cancer, and the impact of such a consultation on treatment. Materials and Methods: Patients aged 70 and older seen at the Lynn Cancer Institute (LCI) with a documented ma lignancy were eligible. Geriatric screening information and the oncologists pre-consultation treatment plan were sent to Moffitt. A search for similar patients was done in TCC and additional information retrieved from Electronic Medical Records. A report summarizing the data was sent and the utility of such a consultation was assessed per email after the treatment decision. Results: Thirty one patients were included. The geriatric screening was positive in 87.1% (27) of them. The oncogeriatric consultation took on average 2.2 working days. It influenced treatment in 38.7% (12), and modified it in 19.4% (6). The consultation was perceived as “somewhat” to “very useful” in 83.9% (26). Conclusion: This study establishes a proof of concept of the feasibility of real time use of Big Data for clinical practice. The geriatric screening and the consultation report influenced treatment in 38.7% of cases and modified it in 19.4%, which compares very well with oncogeriatric literature. Additional steps are needed to render it financially and clinically viable.
Keywords: Electronic database ، Electronic consultation ، Big Data ، Cancer ، Elderly ، Geriatric oncology ، Personalized medicine ، Precision medicine، Total Cancer Care ، Health & Research Informatics
Operating an environmentally sustainable city using fine dust level big data measured at individual elementary schools
مدیریت یک شهر سازگار با محیط زیست با استفاده از داده های بزرگ گرد و غبار، اندازه گیری شده در مدارس ابتدایی فردی-2018
As the problem of fine dust pollution becomes increasingly serious in South Korea, the country is becoming more interested in obtaining information on fine dust levels. Fine dust level data are sufficiently local to make regional forecasting meaningless. Thus, this study proposes an alternative measurement technique to minimize differ ences between published and perceived levels of fine dusts. Owing to the large variations in the fine dust levels within urban areas, it is very difficult to provide measurements that are sufficiently area-representative. Because infants and elementary school students are more sensitive to fine dust than adults, it is useful to construct large data sets of measurements of fine dust levels at elementary schools. In Korea, the distribution of elementary schools is consistent with population density, which is useful for analyzing local differences in the fine dust levels in urban areas. This study will provide a basis for big data application to public health policy and infographics using color fuzzy model.
Keywords: Fine dust ، Big data ، Sustainable city ، Public health policy ، Infographics ، Color fuzzy model