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Analysis and visualization of disease courses in a semantically-enabled cancer registry

Overview of attention for article published in Journal of Biomedical Semantics, September 2017
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Title
Analysis and visualization of disease courses in a semantically-enabled cancer registry
Published in
Journal of Biomedical Semantics, September 2017
DOI 10.1186/s13326-017-0154-9
Pubmed ID
Authors

Angel Esteban-Gil, Jesualdo Tomás Fernández-Breis, Martin Boeker

Abstract

Regional and epidemiological cancer registries are important for cancer research and the quality management of cancer treatment. Many technological solutions are available to collect and analyse data for cancer registries nowadays. However, the lack of a well-defined common semantic model is a problem when user-defined analyses and data linking to external resources are required. The objectives of this study are: (1) design of a semantic model for local cancer registries; (2) development of a semantically-enabled cancer registry based on this model; and (3) semantic exploitation of the cancer registry for analysing and visualising disease courses. Our proposal is based on our previous results and experience working with semantic technologies. Data stored in a cancer registry database were transformed into RDF employing a process driven by OWL ontologies. The semantic representation of the data was then processed to extract semantic patient profiles, which were exploited by means of SPARQL queries to identify groups of similar patients and to analyse the disease timelines of patients. Based on the requirements analysis, we have produced a draft of an ontology that models the semantics of a local cancer registry in a pragmatic extensible way. We have implemented a Semantic Web platform that allows transforming and storing data from cancer registries in RDF. This platform also permits users to formulate incremental user-defined queries through a graphical user interface. The query results can be displayed in several customisable ways. The complex disease timelines of individual patients can be clearly represented. Different events, e.g. different therapies and disease courses, are presented according to their temporal and causal relations. The presented platform is an example of the parallel development of ontologies and applications that take advantage of semantic web technologies in the medical field. The semantic structure of the representation renders it easy to analyse key figures of the patients and their evolution at different granularity levels.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Master 7 14%
Student > Postgraduate 4 8%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Other 11 22%
Unknown 10 20%
Readers by discipline Count As %
Computer Science 25 50%
Medicine and Dentistry 6 12%
Business, Management and Accounting 1 2%
Nursing and Health Professions 1 2%
Agricultural and Biological Sciences 1 2%
Other 5 10%
Unknown 11 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 October 2017.
All research outputs
#15,481,888
of 23,006,268 outputs
Outputs from Journal of Biomedical Semantics
#238
of 364 outputs
Outputs of similar age
#200,971
of 321,103 outputs
Outputs of similar age from Journal of Biomedical Semantics
#9
of 20 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 21st percentile – i.e., 21% 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 321,103 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.