BigData Analysis in Healthcare: Apache Hadoop , Apache spark and Apache Flink

Nazari, Elham; Shahriari, Mohammad Hasan; Tabesh, Hamed

doi:10.30699/fhi.v8i1.180

Cited by 46 publications

(18 citation statements)

References 31 publications

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“…Flink allows users to store data in memory and load them several times. A mechanism provides continuous fault tolerance for restoring the flow of data, and in case of a failure, it can return to snapshots of the system and be used to rebuild the missing data set (119)(120)(121). Given its popularity, Python and its growing number of packages should also be taken into consideration, especially with deep learning, for which specialized languages, such as Tensor flow is necessary (122).…”

Section: Discussionmentioning

confidence: 99%

A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review

Nazari

Chang

Deldar

et al. 2020

ircmj

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Introduction: Decision fusion has emerged as a data management technique due to the diversity and scalability of data in health care. This first-scope review aimed to investigate the use of this technique in health care. Materials and Methods: A query was carried out on PubMed, Science Direct, and EMBASE within 1960-2017 using such keywords as decision fusion, information fusion, symbolic fusion, distributed decisions, expert fusion, and sensor fusion, in conjunction with med-* and health-care. The articles were analyzed in terms of methodology and results. Results: The literature search yielded 106 articles. Based on the results, in the field of health care, the articles were related to image processing (29%), sensors (22%), diagnosis area(10%), biology (6%), health informatics (8%), and signal process (15%). The majority of articles were published in 2011, 2012, and 2015, and the USA had the largest number of articles. Most of the articles were about engineering and basic sciences. Regarding healthcare, the majority of studies were conducted on the diagnosis of diseases (80%), while 9% and 11% of articles were about prevention and treatment, respectively. These studies applied the following methods: intelligent methods (44%), new methods (36%), probabilistic (13%), and evidential methods (7%). The dataset was as follows: research project data (49%), online dataset (42%), and simulation (9%). Furthermore, 49% of articles mentioned the applied software, among which classification and interpretation were reportedly the most and the least used methods. Discussion and Conclusion: Decision fusion is a holistic approach to evaluate all areas of health care and elucidate diverse techniques that can lead to improved quality of care. Innovation: This article is the first scope review article about the application of the decision fusion technique in the field of health care, building on an established protocol. Decision fusion can reduce the cost of care and improve the quality of health care provision. Therefore, this article can help care providers understand the benefits of this technique and overcome challenges in adopting decision fusion technology.

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Section: Discussionmentioning

confidence: 99%

A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review

Nazari

Chang

Deldar

et al. 2020

ircmj

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“…Considering the importance of focusing on the Big Data Area [49] and providing a lot of frameworks in this area, it is recommended that other important health care sectors provide frameworks in this regard, and it is also recommended in future studies of other widely used tools for software engineering in the field of health care Including modeling, simulations, etc., are being investigated, introduced and used [50].…”

Section: Discussionmentioning

confidence: 99%

Applications of Framework In Health Care: A Survey

Nazari

Shahriari

Tabesh

2019

Front Health Inform

Self Cite

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Introduction: The use of healthcare frameworks and, in particular, policy makers is crucial for designing and evaluating systems. Frameworks provide the ability to measure and compare health system functions in different countries in order to make better and more meaningful decisions, to make comparisons within and between countries, identifying gaps, and sharing information. Researchers also have the ability to use the dimensions of the frameworks to measure progress over time. Due to the importance of the subject, the purpose of this study is to describe the framework concepts and the introduction of framework applications in the field of health care.Material and Methods: This study is based on a search of the ProQuest, PubMed, Google Scholar, Science Direct, Scopus, IranMedex, Irandoc, Magiran, ParsMedline and Scientific Information Database (SID) databases, as well as the study of specialized keyword web sites and the standard was done. After a thorough study, 50 sources were selected according to the study objectives and were used to formulate the final article.Results: The framework can be used to manage health system investments, identify important research areas in the field of health, and define new and useful research.Conclusion: Given the importance of the health framework, the need to provide a framework for other critical health care sectors is essential.

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“…For example, in [36] the authors study the financial problem in data from the National Stock Exchange of India (NSE) for a period of one year, using ARMA, ARIMA, ARCH and GARCH models, using Apache Spark for processing. Works such as [37] predict traffic flow through time series with Spark and in others, such as in [38], the authors work with time series related to health care data. However, most of the literature we find are limited to the analysis of time series, but do not provide an architecture that integrates the entire flow of data from sending the data from the IoT to providing a result to the user or interested entity.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

et al. 2019

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The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study.

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BigData Analysis in Healthcare: Apache Hadoop , Apache spark and Apache Flink

Cited by 46 publications

References 31 publications

A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review

A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review

Applications of Framework In Health Care: A Survey

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

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