2014
Portela, F.; Santos, M. Filipe; Silva, A.; Rua, F.; Abelha, A.; Machado, J.
Preventing patient cardiac arrhythmias by using data mining techniques Proceedings Article
In: pp. 165-170, Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 9781479940844, (cited By 18; Conference of 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 ; Conference Date: 8 December 2014 Through 10 December 2014; Conference Code:111205).
Abstract | Links | BibTeX | Etiquetas: Biomedical engineering; Diseases, Cardiac arrhythmia; Input variables; Online learning; Patient admissions; Patient condition; Predictive data mining; Real-time models; Vital sign, Data mining
@inproceedings{Portela2014165,
title = {Preventing patient cardiac arrhythmias by using data mining techniques},
author = {F. Portela and M. Filipe Santos and A. Silva and F. Rua and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925666219&doi=10.1109%2fIECBES.2014.7047478&partnerID=40&md5=251834d68395814ab7a32a47dfef6a59},
doi = {10.1109/IECBES.2014.7047478},
isbn = {9781479940844},
year = {2014},
date = {2014-01-01},
journal = {IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet"},
pages = {165-170},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Cardiac Arrhythmia (CA) is very dangerous and can significantly undermine patient condition. New tools are fundamental to forecast and to prevent possible critical situations. In order to help clinicians acting proactively, predictive data mining real-time models were induced using online-learning. As input variables were considered those acquired at the patient admission and complementary variables (vital signs, laboratory results, therapeutics) hourly collected. The results are very motivating; sensitivity near to 95% was obtained when using Support Vector Machines. The approach explored in this work reveals to be an interesting contribution to the healthcare in terms of predicting CA and a good direction to be further explored. © 2014 IEEE.},
note = {cited By 18; Conference of 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 ; Conference Date: 8 December 2014 Through 10 December 2014; Conference Code:111205},
keywords = {Biomedical engineering; Diseases, Cardiac arrhythmia; Input variables; Online learning; Patient admissions; Patient condition; Predictive data mining; Real-time models; Vital sign, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Cardiac Arrhythmia (CA) is very dangerous and can significantly undermine patient condition. New tools are fundamental to forecast and to prevent possible critical situations. In order to help clinicians acting proactively, predictive data mining real-time models were induced using online-learning. As input variables were considered those acquired at the patient admission and complementary variables (vital signs, laboratory results, therapeutics) hourly collected. The results are very motivating; sensitivity near to 95% was obtained when using Support Vector Machines. The approach explored in this work reveals to be an interesting contribution to the healthcare in terms of predicting CA and a good direction to be further explored. © 2014 IEEE.