An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models. Li, F. & Yu, H. Journal of the American Medical Informatics Association, 26(7):646–654, July, 2019. ![An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex AbstractObjective. We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-
@article{li_investigation_2019,
title = {An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models},
volume = {26},
url = {https://academic.oup.com/jamia/article/26/7/646/5426087},
doi = {10.1093/jamia/ocz018},
abstract = {AbstractObjective. We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-},
language = {en},
number = {7},
urldate = {2019-12-09},
journal = {Journal of the American Medical Informatics Association},
author = {Li, Fei and Yu, Hong},
month = jul,
year = {2019},
pages = {646--654},
}
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