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Examining database persistence of ISO/EN 13606 standardized electronic health record extracts: relational vs. NoSQL approaches

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2017
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  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

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5 tweeters

Citations

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11 Dimensions

Readers on

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83 Mendeley
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Title
Examining database persistence of ISO/EN 13606 standardized electronic health record extracts: relational vs. NoSQL approaches
Published in
BMC Medical Informatics and Decision Making, August 2017
DOI 10.1186/s12911-017-0515-4
Pubmed ID
Authors

Ricardo Sánchez-de-Madariaga, Adolfo Muñoz, Raimundo Lozano-Rubí, Pablo Serrano-Balazote, Antonio L. Castro, Oscar Moreno, Mario Pascual

Abstract

The objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record XML extracts, both in isolation and concurrently. NoSQL database systems have recently attracted much attention, but few studies in the literature address their direct comparison with relational databases when applied to build the persistence layer of a standardized medical information system. One relational and two NoSQL databases (one document-based and one native XML database) of three different sizes have been created in order to evaluate and compare the response times (algorithmic complexity) of six different complexity growing queries, which have been performed on them. Similar appropriate results available in the literature have also been considered. Relational and non-relational NoSQL database systems show almost linear algorithmic complexity query execution. However, they show very different linear slopes, the former being much steeper than the two latter. Document-based NoSQL databases perform better in concurrency than in isolation, and also better than relational databases in concurrency. Non-relational NoSQL databases seem to be more appropriate than standard relational SQL databases when database size is extremely high (secondary use, research applications). Document-based NoSQL databases perform in general better than native XML NoSQL databases. EHR extracts visualization and edition are also document-based tasks more appropriate to NoSQL database systems. However, the appropriate database solution much depends on each particular situation and specific problem.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 19%
Student > Ph. D. Student 12 14%
Researcher 11 13%
Student > Bachelor 6 7%
Other 5 6%
Other 11 13%
Unknown 22 27%
Readers by discipline Count As %
Computer Science 30 36%
Engineering 11 13%
Medicine and Dentistry 9 11%
Agricultural and Biological Sciences 2 2%
Business, Management and Accounting 2 2%
Other 6 7%
Unknown 23 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 February 2019.
All research outputs
#5,071,266
of 16,647,928 outputs
Outputs from BMC Medical Informatics and Decision Making
#563
of 1,521 outputs
Outputs of similar age
#95,054
of 276,252 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 16,647,928 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,521 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 276,252 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them