@inproceedings{suster-etal-2017-short,
title = "A Short Review of Ethical Challenges in Clinical Natural Language Processing",
author = "{\v{S}}uster, Simon and
Tulkens, St{\'e}phan and
Daelemans, Walter",
editor = "Hovy, Dirk and
Spruit, Shannon and
Mitchell, Margaret and
Bender, Emily M. and
Strube, Michael and
Wallach, Hanna",
booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1610",
doi = "10.18653/v1/W17-1610",
pages = "80--87",
abstract = "Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.",
}
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%0 Conference Proceedings
%T A Short Review of Ethical Challenges in Clinical Natural Language Processing
%A Šuster, Simon
%A Tulkens, Stéphan
%A Daelemans, Walter
%Y Hovy, Dirk
%Y Spruit, Shannon
%Y Mitchell, Margaret
%Y Bender, Emily M.
%Y Strube, Michael
%Y Wallach, Hanna
%S Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F suster-etal-2017-short
%X Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.
%R 10.18653/v1/W17-1610
%U https://aclanthology.org/W17-1610
%U https://doi.org/10.18653/v1/W17-1610
%P 80-87
Markdown (Informal)
[A Short Review of Ethical Challenges in Clinical Natural Language Processing](https://aclanthology.org/W17-1610) (Šuster et al., EthNLP 2017)
ACL