Active machine learning to increase annotation efficiency in classifying vital sign events as artifact or real alerts in continuous noninvasive monitoring - Robotics Institute Carnegie Mellon University

Active machine learning to increase annotation efficiency in classifying vital sign events as artifact or real alerts in continuous noninvasive monitoring

Marilyn Hravnak, Lujie Chen, Madaline Fiterau, Artur Dubrawski, Gilles Clermont, Mathieu Guillame-Bert, Eliezer Bose, and Michael R. Pinsky
Journal Article, American Journal of Respiratory and Critical Care Medicine, Vol. 189, pp. 367, May, 2014

BibTeX

@article{Hravnak-2014-121709,
author = {Marilyn Hravnak and Lujie Chen and Madaline Fiterau and Artur Dubrawski and Gilles Clermont and Mathieu Guillame-Bert and Eliezer Bose and Michael R. Pinsky},
title = {Active machine learning to increase annotation efficiency in classifying vital sign events as artifact or real alerts in continuous noninvasive monitoring},
journal = {American Journal of Respiratory and Critical Care Medicine},
year = {2014},
month = {May},
volume = {189},
pages = {367},
}
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