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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Helgheim, B. I., Maia, R., Ferreira, J. C. & Martins, A. L. (2019). Merging data diversity of clinical medical records to improve effectiveness. International Journal of Environmental Research and Public Health. 16 (5)
Exportar Referência (IEEE) 
B. I. Helgheim et al.,  "Merging data diversity of clinical medical records to improve effectiveness", in Int. Journal of Environmental Research and Public Health, vol. 16, no. 5, 2019
Exportar BibTeX 
@article{helgheim2019_1714959386597,
	author = "Helgheim, B. I. and Maia, R. and Ferreira, J. C. and Martins, A. L.",
	title = "Merging data diversity of clinical medical records to improve effectiveness",
	journal = "International Journal of Environmental Research and Public Health",
	year = "2019",
	volume = "16",
	number = "5",
	doi = "10.3390/ijerph16050769",
	url = "https://www.mdpi.com/1660-4601/16/5/769"
}
Exportar RIS 
TY  - JOUR
TI  - Merging data diversity of clinical medical records to improve effectiveness
T2  - International Journal of Environmental Research and Public Health
VL  - 16
IS  - 5
AU  - Helgheim, B. I.
AU  - Maia, R.
AU  - Ferreira, J. C.
AU  - Martins, A. L.
PY  - 2019
SN  - 1660-4601
DO  - 10.3390/ijerph16050769
UR  - https://www.mdpi.com/1660-4601/16/5/769
AB  - Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.
ER  -