@inproceedings{salloum-etal-2017-automated,
title = "Automated Preamble Detection in Dictated Medical Reports",
author = "Salloum, Wael and
Finley, Greg and
Edwards, Erik and
Miller, Mark and
Suendermann-Oeft, David",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2336",
doi = "10.18653/v1/W17-2336",
pages = "287--295",
abstract = "Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.",
}
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<abstract>Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.</abstract>
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%0 Conference Proceedings
%T Automated Preamble Detection in Dictated Medical Reports
%A Salloum, Wael
%A Finley, Greg
%A Edwards, Erik
%A Miller, Mark
%A Suendermann-Oeft, David
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F salloum-etal-2017-automated
%X Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.
%R 10.18653/v1/W17-2336
%U https://aclanthology.org/W17-2336
%U https://doi.org/10.18653/v1/W17-2336
%P 287-295
Markdown (Informal)
[Automated Preamble Detection in Dictated Medical Reports](https://aclanthology.org/W17-2336) (Salloum et al., BioNLP 2017)
ACL