2015
Oliveira, S.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.; Silva, Á.; Rua, F.
Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables Proceedings Article
Em: F., Costa E. Machado P. Pereira (Ed.): pp. 122-127, Springer Verlag, 2015, ISSN: 03029743, (cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Correlation methods; Hospital data processing; Intensive care units, Barotrauma; Clustering; Davies-Bouldin index; Intensive-care patients; Partitioning around medoids; Patient data; Plateau pressures; Similarity, Data mining
@inproceedings{Oliveira2015122,
title = {Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},
editor = {Costa E. Machado P. Pereira F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945970524&doi=10.1007%2f978-3-319-23485-4_13&partnerID=40&md5=8d9e4358d2a397a7ddba9da8359d63bf},
doi = {10.1007/978-3-319-23485-4_13},
issn = {03029743},
year = {2015},
date = {2015-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9273},
pages = {122-127},
publisher = {Springer Verlag},
abstract = {Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clustering have been considered k-means and k-medoids algortihms (Partitioning Around Medoids). The best model induced presented a Davies-Bouldin Index of 0.64. This model identifies the variables that have more similarity among the variables monitored by the ventilators and the occurrence of barotrauma. © Springer International Publishing Switzerland 2015.},
note = {cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439},
keywords = {Artificial intelligence; Correlation methods; Hospital data processing; Intensive care units, Barotrauma; Clustering; Davies-Bouldin index; Intensive-care patients; Partitioning around medoids; Patient data; Plateau pressures; Similarity, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}