2017
Rodrigues, M.; Peixoto, H.; Esteves, M.; Machado, J.; Abelha,
Understanding Stroke in Dialysis and Chronic Kidney Disease Proceedings Article
Em: E., Shakshuki (Ed.): pp. 591-596, Elsevier B.V., 2017, ISSN: 18770509, (cited By 11; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Blood; Classification (of information); Data communication systems; Decision support systems; Dialysis; Health care; Learning algorithms; Learning systems; Risk assessment; Statistical tests, Blood analysis; Blood test; Chronic kidney disease; Conference programs; Data mining models; Peer review; Peritoneal dialysis; Test data, Data mining
@inproceedings{Rodrigues2017591,
title = {Understanding Stroke in Dialysis and Chronic Kidney Disease},
author = {M. Rodrigues and H. Peixoto and M. Esteves and J. Machado and Abelha},
editor = {Shakshuki E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033482094&doi=10.1016%2fj.procs.2017.08.296&partnerID=40&md5=7da878098027c0cfd48debf22d157457},
doi = {10.1016/j.procs.2017.08.296},
issn = {18770509},
year = {2017},
date = {2017-01-01},
journal = {Procedia Computer Science},
volume = {113},
pages = {591-596},
publisher = {Elsevier B.V.},
abstract = {Patients with severe kidney failure need to be carefully monitored. One of the many treatments is called Continuous Ambulatory Peritoneal Dialysis (CAPD). This kind of treatment intends to maintain the blood tests as normal as possible. Data Mining and Machine Learning can take a simple and meaningless blood's test data set and build it into a Decision Support System. Through this article, Machine Learning algorithms will be explored with different Data Mining Models in order to extract knowledge and classify a patient with a stroke risk or not, according to their blood analysis. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},
note = {cited By 11; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},
keywords = {Artificial intelligence; Blood; Classification (of information); Data communication systems; Decision support systems; Dialysis; Health care; Learning algorithms; Learning systems; Risk assessment; Statistical tests, Blood analysis; Blood test; Chronic kidney disease; Conference programs; Data mining models; Peer review; Peritoneal dialysis; Test data, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}