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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 20% |
| Kenya | 1 | 20% |
| France | 1 | 20% |
| Spain | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 101 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 19% |
| Student > Master | 16 | 16% |
| Researcher | 12 | 12% |
| Student > Bachelor | 7 | 7% |
| Professor > Associate Professor | 5 | 5% |
| Other | 14 | 14% |
| Unknown | 28 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 32% |
| Engineering | 11 | 11% |
| Business, Management and Accounting | 9 | 9% |
| Medicine and Dentistry | 9 | 9% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 8 | 8% |
| Unknown | 30 | 30% |
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
#7,200,902
of 22,999,744 outputs
Outputs from BMC Medical Informatics and Decision Making
#717
of 2,006 outputs
Outputs of similar age
#114,621
of 318,830 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 41 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 2,006 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 63% 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 318,830 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 63% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.