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. 2017 Sep 29;8(1):46.
doi: 10.1186/s13326-017-0154-9.

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

Angel Esteban-Gil  1 , Jesualdo Tomás Fernández-Breis  2 , Martin Boeker  3
Affiliations

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

Angel Esteban-Gil et al. J Biomed Semantics. .

Abstract

Background: 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.

Results: 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.

Conclusion: 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.

Keywords: Biomedical informatics; Cancer registry; Ontology; Semantic web.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Schema of probability distribution of diagnoses and therapies
Fig. 2
Fig. 2
Schema of semantic profile of a cancer patient
Fig. 3
Fig. 3
Overview of the generation of aggregated disease timeline of a patient group
Fig. 4
Fig. 4
Example of semantic dashboard
Fig. 5
Fig. 5
Overview of the system
Fig. 6
Fig. 6
Dashboard view
Fig. 7
Fig. 7
Excerpt of the timeline representation
Fig. 8
Fig. 8
Ontology-driven searcher view
Fig. 9
Fig. 9
Excerpt of the aggregated disease timeline of a patient group
See this image and copyright information in PMC

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