@inproceedings{zhang-etal-2020-mie,
title = "{MIE}: A Medical Information Extractor towards Medical Dialogues",
author = "Zhang, Yuanzhe and
Jiang, Zhongtao and
Zhang, Tao and
Liu, Shiwan and
Cao, Jiarun and
Liu, Kang and
Liu, Shengping and
Zhao, Jun",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.576",
doi = "10.18653/v1/2020.acl-main.576",
pages = "6460--6469",
abstract = "Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.",
}
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<abstract>Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.</abstract>
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%0 Conference Proceedings
%T MIE: A Medical Information Extractor towards Medical Dialogues
%A Zhang, Yuanzhe
%A Jiang, Zhongtao
%A Zhang, Tao
%A Liu, Shiwan
%A Cao, Jiarun
%A Liu, Kang
%A Liu, Shengping
%A Zhao, Jun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-mie
%X Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.
%R 10.18653/v1/2020.acl-main.576
%U https://aclanthology.org/2020.acl-main.576
%U https://doi.org/10.18653/v1/2020.acl-main.576
%P 6460-6469
Markdown (Informal)
[MIE: A Medical Information Extractor towards Medical Dialogues](https://aclanthology.org/2020.acl-main.576) (Zhang et al., ACL 2020)
ACL
- Yuanzhe Zhang, Zhongtao Jiang, Tao Zhang, Shiwan Liu, Jiarun Cao, Kang Liu, Shengping Liu, and Jun Zhao. 2020. MIE: A Medical Information Extractor towards Medical Dialogues. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6460–6469, Online. Association for Computational Linguistics.