@inproceedings{ding-etal-2020-methods,
title = "Methods for Extracting Information from Messages from Primary Care Providers to Specialists",
author = "Ding, Xiyu and
Barnett, Michael and
Mehrotra, Ateev and
Miller, Timothy",
editor = "Bhatia, Parminder and
Lin, Steven and
Gangadharaiah, Rashmi and
Wallace, Byron and
Shafran, Izhak and
Shivade, Chaitanya and
Du, Nan and
Diab, Mona",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpmc-1.1",
doi = "10.18653/v1/2020.nlpmc-1.1",
pages = "1--6",
abstract = "Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems. Understanding the usage patterns of eConsult system is an important part of improving specialist efficiency. In this work, we develop and apply classifiers to a dataset of eConsult questions from primary care providers to specialists, classifying the messages for how they were triaged by the specialist office, and the underlying type of clinical question posed by the primary care provider. We show that pre-trained transformer models are strong baselines, with improving performance from domain-specific training and shared representations.",
}
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%0 Conference Proceedings
%T Methods for Extracting Information from Messages from Primary Care Providers to Specialists
%A Ding, Xiyu
%A Barnett, Michael
%A Mehrotra, Ateev
%A Miller, Timothy
%Y Bhatia, Parminder
%Y Lin, Steven
%Y Gangadharaiah, Rashmi
%Y Wallace, Byron
%Y Shafran, Izhak
%Y Shivade, Chaitanya
%Y Du, Nan
%Y Diab, Mona
%S Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ding-etal-2020-methods
%X Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems. Understanding the usage patterns of eConsult system is an important part of improving specialist efficiency. In this work, we develop and apply classifiers to a dataset of eConsult questions from primary care providers to specialists, classifying the messages for how they were triaged by the specialist office, and the underlying type of clinical question posed by the primary care provider. We show that pre-trained transformer models are strong baselines, with improving performance from domain-specific training and shared representations.
%R 10.18653/v1/2020.nlpmc-1.1
%U https://aclanthology.org/2020.nlpmc-1.1
%U https://doi.org/10.18653/v1/2020.nlpmc-1.1
%P 1-6
Markdown (Informal)
[Methods for Extracting Information from Messages from Primary Care Providers to Specialists](https://aclanthology.org/2020.nlpmc-1.1) (Ding et al., NLPMC 2020)
ACL