Designing a health data management system based hadoop-agent

Khennou, Fadoua; Khamlichi, Youness Idrissi; Chaoui, Nour El Houda

doi:10.1109/cist.2016.7804983

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…There is an unmistakable need for robust standardized procedures, formats, structured data, and nomenclature ontologies to guarantee unwavering quality and efficiency (Rosenbaum L, 2015) [37] However this data structure poses a problem to data analysis as the data provided by EHR is a combination of i) Structured, ii) Unstructured and ii) Semi structured at the same time. This volume and diversity of healthcare data pushes us to look for solutions offered by Big Data in storing, processing and analyzing data for best care of the patients (JMER, 2016) [6] and better health planning (Fadoua Khennou, Youness Idrissi Khamlichi, et al, 2016) [7].…”

Section: Methodsmentioning

confidence: 99%

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Technological and Scientific Developments Towards Use of Big Data in Health Data Management – An Overview

Bengeri¹

2022

IJSREM

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The healthcare industry has been using data and technology driven innovation for a long time now with Evidence Based Medicine (EBM) being at the helm. Effectively using technology is vital towards successful data management in healthcare. A convenient approach that can collect, store, evaluate, and analyze health data will be beneficial to all the stakeholders in the healthcare delivery chain. With the advancement, clubbed with the availability and affordability of technology, it has become an effective tool for providing the right- care backed with accurate, consolidated evidence. Such approach not only helps in improving the quality of care, it also helps in early detection of diseases and effective treatment, Big Data is providing the necessary data management tools for the same. A simple Extract-Transform-Load (ETL) procedure using Hadoop can be used. As Big data analyses large amounts of data to uncover hidden patterns, correlations and other insights, its role in making the right, predictive decisions for quality care of the patients and better health planning is possible. Big data technologies such as Hadoop bring significant cost advantages when it comes to storing large amounts of data and combined with the speed of Hadoop and in-memory analytics, with the ability to analyze new sources of data, we are able to analyze information instantly – and make decisions which is very essential in the healthcare provider segment. Hadoop does not only store, but also process any and many file data: big or small, can be plain text files or binary files like images and even multiple different version of some particular data format across different time periods. It consists of three core components: a distributed file system, a parallel programming framework, and a resource/job management system. In healthcare, patient records, health plans, insurance information and other types of information is extremely difficult to manage – but, this very data is full of key insights, the need is for an effective data management and analytics that can be pragmatic. By analyzing large amounts of information – both structured and unstructured – rapidly, health care providers can provide lifesaving diagnoses. Keywords: Big Data, EBM, ETL, Hadoop

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Section: Methodsmentioning

confidence: 99%

“…They also describe solution to the problems of storing and processing of health data using Hadoop ecosystem. In addition, they have proposed the concept of agents that play a vital role in smooth sharing of the medical data across different organizations [7].…”

Section: Introductionmentioning

confidence: 99%

Technological and Scientific Developments Towards Use of Big Data in Health Data Management – An Overview

Bengeri¹

2022

IJSREM

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“…The importance of health informatics, or E-health as it is commonly known, has increased dramatically through the big data revolution. The discipline of medical informatics is involved in the way of presenting new and efficient methods of collection, storage and analysis of medical data, thus improve diagnosis and treatment of many patients [1].…”

Section: Introductionmentioning

confidence: 99%