2017
Ribeiro, A.; Portela, F.; Santos, M.; Abelha, A.; Machado, J.; Rua, F.
Patients' admissions in intensive care units: A clustering overview Journal Article
Em: Information (Switzerland), vol. 8, não 1, 2017, ISSN: 20782489, (cited By 5).
Resumo | Links | BibTeX | Etiquetas: Admissions; Clustering; Clustering techniques; Critical environment; Davies-Bouldin index; Health care professionals; INTCare system; Intensive care; INTcare system, Artificial intelligence; Data mining; Decision support systems; Health care; Information management, Intensive care units
@article{Ribeiro2017,
title = {Patients' admissions in intensive care units: A clustering overview},
author = {A. Ribeiro and F. Portela and M. Santos and A. Abelha and J. Machado and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014216800&doi=10.3390%2finfo8010023&partnerID=40&md5=851d20dc86a037782c7a789bd21e6818},
doi = {10.3390/info8010023},
issn = {20782489},
year = {2017},
date = {2017-01-01},
journal = {Information (Switzerland)},
volume = {8},
number = {1},
publisher = {MDPI AG},
abstract = {Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU management is evident. It is proven that unplanned and prolonged admission to the ICU is not only prejudicial to a patient's health, but also such a situation implies a readjustment of ICU resources, including beds, doctors, nurses, financial resources, among others. By discovering the common characteristics of the admitted patients, it is possible to improve these outcomes. In this study clustering techniques were applied to data collected from admitted patients in an intensive care unit. The best results presented a silhouette of 1, with a distance to centroids of 6.2 × 10-17 and a Davies-Bouldin index of -0.652.},
note = {cited By 5},
keywords = {Admissions; Clustering; Clustering techniques; Critical environment; Davies-Bouldin index; Health care professionals; INTCare system; Intensive care; INTcare system, Artificial intelligence; Data mining; Decision support systems; Health care; Information management, Intensive care units},
pubstate = {published},
tppubtype = {article}
}
Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU management is evident. It is proven that unplanned and prolonged admission to the ICU is not only prejudicial to a patient's health, but also such a situation implies a readjustment of ICU resources, including beds, doctors, nurses, financial resources, among others. By discovering the common characteristics of the admitted patients, it is possible to improve these outcomes. In this study clustering techniques were applied to data collected from admitted patients in an intensive care unit. The best results presented a silhouette of 1, with a distance to centroids of 6.2 × 10-17 and a Davies-Bouldin index of -0.652.