| Title |
Autonomic care platform for optimizing query performance
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, October 2013
|
| DOI | 10.1186/1472-6947-13-120 |
| Pubmed ID | |
| Authors |
Kristof Steurbaut, Steven Latré, Johan Decruyenaere, Filip De Turck |
| Abstract |
As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 1 | 3% |
| Unknown | 31 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 16% |
| Researcher | 3 | 9% |
| Student > Master | 3 | 9% |
| Other | 2 | 6% |
| Student > Bachelor | 2 | 6% |
| Other | 8 | 25% |
| Unknown | 9 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 13% |
| Computer Science | 4 | 13% |
| Social Sciences | 3 | 9% |
| Nursing and Health Professions | 2 | 6% |
| Engineering | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 11 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 02 November 2013.
All research outputs
#4,001,728
of 22,727,570 outputs
Outputs from BMC Medical Informatics and Decision Making
#347
of 1,985 outputs
Outputs of similar age
#37,696
of 212,562 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 48 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% 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 212,562 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 48 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 68% of its contemporaries.