The Effectiveness of Transfer Learning in Electronic Health Records Data
Abstract
The application of machine learning to clinical data from Electronic Health Records is limited by the scarcity of meaningful labels. Here we present initial results on the application of transfer learning to this problem. We explore the transfer of knowledge from source tasks in which training labels are plentiful but of limited clinical value to more meaningful target tasks that have few labels.
Cite
Text
Dubois et al. "The Effectiveness of Transfer Learning in Electronic Health Records Data." International Conference on Learning Representations, 2017.Markdown
[Dubois et al. "The Effectiveness of Transfer Learning in Electronic Health Records Data." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/dubois2017iclr-effectiveness/)BibTeX
@inproceedings{dubois2017iclr-effectiveness,
title = {{The Effectiveness of Transfer Learning in Electronic Health Records Data}},
author = {Dubois, Sébastien and Romano, Nathanael and Jung, Kenneth and Shah, Nigam and Kale, David C.},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/dubois2017iclr-effectiveness/}
}