2021
DOI: 10.2196/26892
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Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study

Abstract: Background Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associat… Show more

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Cited by 7 publications
(9 citation statements)
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“…Compared with two commonly used information models named CEM and FHIR, the PhenoSSU model is more suitable for the task of deep phenotyping for two reasons. First, it has been shown that the PhenoSSU model is better at representing phenotype information in medical text than CEM and FHIR models [ 21 ]. Second, the PhenoSSU model puts more focus on characterizing phenotype traits with standardized attribute and value sets; as well, the attribute and value sets of the PhenoSSU model are easier to adjust according to the study-specific corpus.…”
Section: Methodsmentioning
confidence: 99%
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“…Compared with two commonly used information models named CEM and FHIR, the PhenoSSU model is more suitable for the task of deep phenotyping for two reasons. First, it has been shown that the PhenoSSU model is better at representing phenotype information in medical text than CEM and FHIR models [ 21 ]. Second, the PhenoSSU model puts more focus on characterizing phenotype traits with standardized attribute and value sets; as well, the attribute and value sets of the PhenoSSU model are easier to adjust according to the study-specific corpus.…”
Section: Methodsmentioning
confidence: 99%
“…To develop a fine-grained annotated corpus, 1000 Chinese EHRs of respiratory system diseases were manually annotated based on the PhenoSSU model, whose design was based on infectious diseases with a large proportion of respiratory diseases [ 21 ]. These 1000 Chinese EHRs were obtained from the EHR database of the Iiyi website [ 26 ]; all of the patients’ private information in these EHRs have been masked by the Iiyi website.…”
Section: Methodsmentioning
confidence: 99%
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“…In addition to the best paper from Vogt [6], which recommends using a knowledge graph rather than an ontology to represent empirical data in anatomy, there are three other candidate papers about building knowledge graphs for disparate domains [10][11][12].…”
Section: Knowledge Graphmentioning
confidence: 99%