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English Information

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Authors
# Name
1 Rafael Conrado(rafaelconrado@usp.br)
2 Marco Gutierrez(marco.gutierrez@incor.usp.br)
3 Caetano Traina Jr.(caetano@icmc.usp.br)
4 Agma Traina(agma@icmc.usp.br)
5 Mirela Cazzolato(mtcazzolato@gmail.com)

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Reference
# Reference
1 Andrade, M. J. and Medeiros, C. B. (2023). Linking heterogeneous health data sources in Brazil centered on drug leaflet processing. In SBBD 2023, pages 366–371. SBC. DOI: 10.5753/sbbd.2023.233356.
2 Cazzolato, M. T. et al. (2023). Exploratory data analysis in electronic health records graphs: Intuitive features and visualization tools. In CBMS 2023, pages 117–122. IEEE. DOI: 10.1109/CBMS58004.2023.00202.
3 da Costa, F. J. et al. (2022). Dikw4iot: Uma abordagem baseada na hierarquia DIKW para a construção de grafos de conhecimento para integração de dados de IOT. In SBBD 2022, pages 190–202. SBC. DOI: 10.5753/sbbd.2022.224648.
4 de Lima, D. M. et al. (2019). Transforming two decades of ePR data to OMOP CDM for clinical research. In MEDINFO 2019, volume 264, pages 233–237. IOS Press. DOI: 10.3233/SHTI190218.
5 de Souza, E. M. F. et al. (2022). Visualização interativa da evolução de grafos de conhecimento. In SBBD 2022, pages 343–354. SBC. DOI: 10.5753/sbbd.2022.224301.
6 Fidalgo, P. et al. (2022). Star-bridge: a topological multidimensional subgraph analysis to detect fraudulent nodes and rings in telecom networks. In Big Data 2022, pages 2239–2242. DOI: 10.1109/BigData55660.2022.10020714.
7 Gupta, N. et al. (2018). Beyond outlier detection: Lookout for pictorial explanation. In ECML PKDD 2018, volume 11051 of LNCS, pages 122–138. Springer. DOI: 10.1007/978-3-030-10925-7 8.
8 Nouri, M. et al. (2021). VISEMURE: A visual analytics system for making sense of multimorbidity using electronic medical record data. J. Data, 6(8):85. DOI: 10.3390/DATA6080085.
9 OHDSI (2024). The Book of OHDSI — observational health data sciences and informatics. https://ohdsi.github.io/TheBookOfOhdsi/. Last accessed in 27-06-2024.
10 Overhage, J. M. et al. (2011). Validation of a common data model for active safety surveillance research. In Journal JAMIA, volume 19, pages 54–60. DOI: 10.1136/amiajnl-2011-000376.
11 Stang, P. et al. (2010). Advancing the science for active surveillance: Rationale and design for the observational medical outcomes partnership. In Annals of internal medicine, volume 153, pages 600–6. DOI: 10.1059/0003-4819-153-9-201011020-00010.
12 Wang, Y., Peng, Y., and Guo, J. (2024). Enhancing knowledge graph embedding with structure and semantic features. In Appl. Intell., volume 54, pages 2900–2914. DOI: 10.1007/S10489-024-05315-2.
13 Xiao, G. et al. (2023). FHIR-Ontop-OMOP: Querying OMOP clinical databases as fhir-compliant clinical knowledge graphs. volume 3415 of CEUR Workshop, pages 165–166. CEUR-WS.org. DOI: 10.1016/j.jbi.2022.104201.
14 Yang, P. et al. (2024). LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications. Knowl. Based Syst., 284:111323. DOI: 10.1016/J.KNOSYS.2023.111323.