| Autor: |
MEDEIROS, Gabriel H. A., SOUALMIA, Lina F., ZANNI-MERK, Cecilia |
| Zdroj: |
Studies in Health Technology & Informatics; 2024, Vol. 316, p1933-1937, 5p |
| Abstrakt: |
Biomedical data analysis and visualization often demand data experts for each unique health event. There is a clear lack of automatic tools for semantic visualization of the spread of health risks through biomedical data. Illnesses such as coronavirus disease (COVID-19) and Monkeypox spread rampantly around the world before governments could make decisions based on the analysis of such data. We propose the design of a knowledge graph (KG) for spatio-temporal tracking of public health event propagation. To achieve this, we propose the specialization of the Core Propagation Phenomenon Ontology (PropaPhen) into a health-related propagation phenomenon domain ontology. Data from the UMLS and Open Street Maps are suggested for instantiating the proposed knowledge graph. Finally, the results of a use case on COVID-19 data from the World Health Organization are analyzed to evaluate the possibilities of our approach. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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