2023
Lopes, J.; Sousa, R.; Abelha, A.; Machado, J.
Big Data in Healthcare Institutions: An Architecture Proposal Proceedings Article
Em: R., Zeng D. Huang H. Hou (Ed.): pp. 297-311, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (cited By 0; Conference of 11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022 ; Conference Date: 10 December 2022 Through 11 December 2022; Conference Code:295539).
Resumo | Links | BibTeX | Etiquetas: Architecture; Computer architecture; Health care; Information management; Information use; Medical information systems; Real time systems, Average life expectancy; Continuous improvements; Daily lives; Evidence-based medicine; Healthcare information system; Healthcare institutions; Pathogenic mechanisms; Real-time information systems; Risk predictions; Systems architecture, Big data
@inproceedings{Lopes2023297,
title = {Big Data in Healthcare Institutions: An Architecture Proposal},
author = {J. Lopes and R. Sousa and A. Abelha and J. Machado},
editor = {Zeng D. Huang H. Hou R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163403837&doi=10.1007%2f978-3-031-33614-0_20&partnerID=40&md5=32838fc94653d1c8bf46169b58d98913},
doi = {10.1007/978-3-031-33614-0_20},
issn = {18678211},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {480 LNICST},
pages = {297-311},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Healthcare institutions are complex organizations dedicated to providing care to the population. Continuous improvement has made the care provided a factor of excellence in the population, improving people’s daily lives and increasing average life expectancy. Even so, the resulting aging has caused patterns to increase day by day and the paradigm of medicine to shift from reaction to prevention. Often, the principle of evidence-based medicine is compromised by lack of evidence on pathogenic mechanisms, risk prediction, lack of resources, and effective therapeutic strategies. This is even more evident in pandemic situations. The current data management tools (centered in a single machine) do not have an ideal behavior for the processing of large amounts of information. This fact combined with the lack of sensitivity for the health area makes it imminent the need to create and implement an architecture that performs this management and processing effectively. In this sense, this paper aims to study the problem of knowledge construction from Big Data in health institutions. The main goal is to present an architecture that deals with the adversities of the big data universe when applied to health. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022 ; Conference Date: 10 December 2022 Through 11 December 2022; Conference Code:295539},
keywords = {Architecture; Computer architecture; Health care; Information management; Information use; Medical information systems; Real time systems, Average life expectancy; Continuous improvements; Daily lives; Evidence-based medicine; Healthcare information system; Healthcare institutions; Pathogenic mechanisms; Real-time information systems; Risk predictions; Systems architecture, Big data},
pubstate = {published},
tppubtype = {inproceedings}
}
Healthcare institutions are complex organizations dedicated to providing care to the population. Continuous improvement has made the care provided a factor of excellence in the population, improving people’s daily lives and increasing average life expectancy. Even so, the resulting aging has caused patterns to increase day by day and the paradigm of medicine to shift from reaction to prevention. Often, the principle of evidence-based medicine is compromised by lack of evidence on pathogenic mechanisms, risk prediction, lack of resources, and effective therapeutic strategies. This is even more evident in pandemic situations. The current data management tools (centered in a single machine) do not have an ideal behavior for the processing of large amounts of information. This fact combined with the lack of sensitivity for the health area makes it imminent the need to create and implement an architecture that performs this management and processing effectively. In this sense, this paper aims to study the problem of knowledge construction from Big Data in health institutions. The main goal is to present an architecture that deals with the adversities of the big data universe when applied to health. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.