ArticleJournal of biomedical semantics2017
Analysis and visualization of disease courses in a semantically-enabled cancer registry.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph databases in systems biology: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Operational tolerance research in liver transplantation: a bibliometric analysis using a new web resource.Frontiers in research metrics and analytics · 2024Article
- Semantic Data Visualisation for Biomedical Database Catalogues.Healthcare (Basel, Switzerland) · 2022Article
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- A multipurpose TNM stage ontology for cancer registries.Journal of biomedical semantics · 2022Article
- An ontology-based approach for developing a harmonised data-validation tool for European cancer registration.Journal of biomedical semantics · 2021Article
- Intestinal microbiota alterations by dietary exposure to chemicals from food cooking and processing. Application of data science for risk prediction.Computational and structural biotechnology journal · 2021Review
- Knowledge Graph-Enabled Cancer Data Analytics.IEEE journal of biomedical and health informatics · 2020Article
- ColPortal, an integrative multiomic platform for analysing epigenetic interactions in colorectal cancer.Scientific data · 2019Article
- The role of medical registries, potential applications and limitations.Medicine and pharmacy reports · 2019Review
- As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018.Yearbook of medical informatics · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRegional 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.
resultsOur 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.
conclusionThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.