↓ Skip to main content

An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA)

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
40 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA)
Published in
BMC Medical Informatics and Decision Making, December 2017
DOI 10.1186/s12911-017-0568-4
Pubmed ID
Authors

Haitham Maarouf, María Taboada, Hadriana Rodriguez, Manuel Arias, Ángel Sesar, María Jesús Sobrido

Abstract

Electronic rating scales represent an important resource for standardized data collection. However, the ability to exploit reasoning on rating scale data is still limited. The objective of this work is to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. We developed an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. In order to address the modeling challenges of the SARA, we propose to combine the best performances from OpenEHR clinical archetypes, guidelines and ontologies. A scaled-down version of the Human Phenotype Ontology (HPO) was built, extracting the terms that describe the SARA tests from free-text sources. This version of the HPO was then used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Each unit used the most appropriate technology to formally represent the required knowledge. Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by Spinocerebellar Ataxia Type 36 (SCA36). When comparing the performance of our prototype with that of two independent experts, weighted kappa scores ranged from 0.62 to 0.86. The combination of archetypes, phenotype ontologies and electronic information-processing rules can be used to automate the extraction of relevant clinical knowledge from plain scores of rating scales. Our results reveal a substantial degree of agreement between the results achieved by an ontology-aware system and the human experts.

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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 18%
Student > Ph. D. Student 6 15%
Other 4 10%
Student > Bachelor 4 10%
Student > Doctoral Student 3 8%
Other 5 13%
Unknown 11 28%
Readers by discipline Count As %
Computer Science 10 25%
Business, Management and Accounting 4 10%
Neuroscience 3 8%
Medicine and Dentistry 3 8%
Engineering 2 5%
Other 6 15%
Unknown 12 30%

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 23 December 2017.
All research outputs
#6,830,020
of 12,343,843 outputs
Outputs from BMC Medical Informatics and Decision Making
#604
of 1,109 outputs
Outputs of similar age
#152,778
of 347,662 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#24
of 49 outputs
Altmetric has tracked 12,343,843 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,109 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 44th percentile – i.e., 44% 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 347,662 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 54% of its contemporaries.
We're also able to compare this research output to 49 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 51% of its contemporaries.