Big Data Analytics and Processing Platform in Czech Republic Healthcare

Stufi, Martin; Bačić, Boris; Stoimenov, Leonid

doi:10.3390/app10051705

Cited by 16 publications

(5 citation statements)

References 54 publications

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“…A NoSQL model [ 27 ] for storing health data was implemented in the Cloud for distribution purposes. The model’s efficiency was compared against the RDBMS model in terms of querying time, data preparation, flexibility, and extensibility criteria.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications

Kotsilieris¹

2021

Healthcare

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Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape such innovative HIT architectures include: Mobile agents (Mas) and NoSQL technologies. Mobile agents provide an inherent way of tackling distributed problems of accessing heterogeneous and spatially diverse data sources. NoSQL technology gains ground for the development of scalable applications with non-static and open data schema from complex and diverse sources. Methods and Design: This paper conducts a twofold study: It attempts a literature review of the applications based on the mobile agent (MA) and NoSQL technologies for healthcare support services. Subsequently, a pilot system evaluates the NoSQL technology against the relational one within a distributed environment based on mobile agents for information retrieval. Its objective is to study the feasibility of developing systems that will employ ontological data representation and task implementation through mobile agents towards flexible and transparent health data monitoring. Results and Discussion: The articles studied focus on applying mobile agents for patient support and healthcare services provision thus as to make a positive contribution to the treatment of chronic diseases. In addition, attention is put on the design of platform neutral techniques for clinical data gathering and dissemination over NoSQL. The experimental environment was based on the Apache Jena Fuseki NoSQL server and the JAVA Agent DEvelopment Framework -JADE agent platform. The results reveal that the NoSQL implementation outperforms the standard relational one.

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Section: Methodsmentioning

confidence: 99%

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications

Kotsilieris¹

2021

Healthcare

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“…Such tools are frequently used in conjunction with several open-source tools, eg, R, Python, and scalable machine learning tools, and commercially available tools, eg, MS SQL, Tableau, and Oracle Rdb. 27 Table 3 lists some examples of different tools along with their advantages and limitations.…”

Section: Cloud Computing In Bioinformaticsmentioning

confidence: 99%

Cloud Computing Enabled Big Multi-Omics Data Analytics

Koppad

Annappa

Gkoutos

et al. 2021

Bioinform Biol Insights

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High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.

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“…For example, Britain’s comprehensive platform integrates and applies data from 12 categories, including health, medical care, transportation, and environment, to support government decision-making [ 3 ]. The Big Data Analytics Platform built by the Czech Republic meets the data analysis requirements of its national public health service [ 4 ]. In recent years, China has built a national-level health information platform that can be connected to provincial-level health information platforms for data integration [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application

Wang¹,

Li²,

Tao³

et al. 2022

JMIR Med Inform

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Background With the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care. Objective We aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security. Methods The project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master–slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established. Results After 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model—from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month). Conclusions The platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research.

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Big Data Analytics and Processing Platform in Czech Republic Healthcare

Cited by 16 publications

References 54 publications

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications

Cloud Computing Enabled Big Multi-Omics Data Analytics

Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application

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