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Developing a similarity searching module for patient safety event reporting system using semantic similarity measures

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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Mentioned by

twitter
3 tweeters

Citations

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4 Dimensions

Readers on

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25 Mendeley
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Title
Developing a similarity searching module for patient safety event reporting system using semantic similarity measures
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0467-8
Pubmed ID
Authors

Hong Kang, Yang Gong

Abstract

The most important knowledge in the field of patient safety is regarding the prevention and reduction of patient safety events (PSE) during treatment and care. The similarities and patterns among the events may otherwise go unnoticed if they are not properly reported and analyzed. There is an urgent need for developing a PSE reporting system that can dynamically measure the similarities of the events and thus promote event analysis and learning effect. In this study, three prevailing algorithms of semantic similarity were implemented to measure the similarities of the 366 PSE annotated by the taxonomy of The Agency for Healthcare Research and Quality (AHRQ). The performance of each algorithm was then evaluated by a group of domain experts based on a 4-point Likert scale. The consistency between the scales of the algorithms and experts was measured and compared with the scales randomly assigned. The similarity algorithms and scores, as a self-learning and self-updating module, were then integrated into the system. The result shows that the similarity scores reflect a high consistency with the experts' review than those randomly assigned. Moreover, incorporating the algorithms into our reporting system enables a mechanism to learn and update based upon PSE similarity. In conclusion, integrating semantic similarity algorithms into a PSE reporting system can help us learn from previous events and provide timely knowledge support to the reporters. With the knowledge base in the PSE domain, the new generation reporting system holds promise in educating healthcare providers and preventing the recurrence and serious consequences of PSE.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 20%
Other 4 16%
Professor 3 12%
Student > Bachelor 3 12%
Student > Ph. D. Student 3 12%
Other 6 24%
Unknown 1 4%
Readers by discipline Count As %
Medicine and Dentistry 9 36%
Computer Science 6 24%
Engineering 3 12%
Nursing and Health Professions 3 12%
Economics, Econometrics and Finance 1 4%
Other 2 8%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 July 2017.
All research outputs
#8,526,942
of 14,161,931 outputs
Outputs from BMC Medical Informatics and Decision Making
#845
of 1,312 outputs
Outputs of similar age
#142,496
of 264,321 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 7 outputs
Altmetric has tracked 14,161,931 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 264,321 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.