ArticleYearbook of medical informatics2019
Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.
Article in Yearbook of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Semantic Explanation for Malaria Diagnosis: Comparing Human and Machine Generated Annotations forIEEE open journal of engineering in medicine and biology · 2026Article
- From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Development and validation of the early warning system scores ontology.Journal of biomedical semantics · 2023Article
- The PRISM semantic cohort builder: a novel tool to search and access clinical data in TCIA imaging collections.Physics in medicine and biology · 2022Article
- Assessing the Need for Semantic Data Integration for Surgical Biobanks-A Knowledge Representation Perspective.Journal of personalized medicine · 2022Article
- A knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry.Clinical and translational radiation oncology · 2021Article
- Why Is the Electronic Health Record So Challenging for Research and Clinical Care?Methods of information in medicine · 2021Article
- [Development of an Optimized Deep Learning Model for Medical Imaging].Taehan Yongsang Uihakhoe chi · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesThere exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).
methodsWe employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.
resultsWe demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.
conclusionsWe conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
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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.