2016
Ribeiro, A.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.; Rua, F.
Patients' admissions in intensive care units: A clustering overview Proceedings Article
Em: E., Huemer C. Poels G. Kornyshova (Ed.): pp. 38-44, Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509032310, (cited By 0; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364).
Resumo | Links | BibTeX | Etiquetas: admissions; clustering; Clustering techniques; Davies-Bouldin index; Health care professionals; INTCare; Intensive care, Artificial intelligence; Data mining; Information science; Intensive care units, Decision support systems
@inproceedings{Ribeiro201638,
title = {Patients' admissions in intensive care units: A clustering overview},
author = {A. Ribeiro and F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua},
editor = {Huemer C. Poels G. Kornyshova E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010284769&doi=10.1109%2fCBI.2016.48&partnerID=40&md5=0578138e0e8a81352aed1d0cd29a6ccf},
doi = {10.1109/CBI.2016.48},
isbn = {9781509032310},
year = {2016},
date = {2016-01-01},
journal = {Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics},
volume = {2},
pages = {38-44},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Intensive Care is one of the most critical areas ofmedicine. Its multidisciplinary nature makes it a very wide area, requiring all types of healthcare professionals. Given the criticalenvironment of intensive care units, it becomes evident the need touse technology of decision support systems to improve healthcareservices and Intensive Care Units management. By discovering thecommon characteristics of the admitted patients it is possible toimprove these outcomes. In this study clustering techniques wereapplied to data collected from admitted patients in Intensive CareUnit. The best results presented a Silhouette of 1, with a distance tocentroids of 6.2e-17 and a Davies-Bouldin index of -0.652. © 2016 IEEE.},
note = {cited By 0; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364},
keywords = {admissions; clustering; Clustering techniques; Davies-Bouldin index; Health care professionals; INTCare; Intensive care, Artificial intelligence; Data mining; Information science; Intensive care units, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Intensive Care is one of the most critical areas ofmedicine. Its multidisciplinary nature makes it a very wide area, requiring all types of healthcare professionals. Given the criticalenvironment of intensive care units, it becomes evident the need touse technology of decision support systems to improve healthcareservices and Intensive Care Units management. By discovering thecommon characteristics of the admitted patients it is possible toimprove these outcomes. In this study clustering techniques wereapplied to data collected from admitted patients in Intensive CareUnit. The best results presented a Silhouette of 1, with a distance tocentroids of 6.2e-17 and a Davies-Bouldin index of -0.652. © 2016 IEEE.