Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization

Wen-wai Yim; Meliha Yetisgen-Yildiz

doi:10.18653/v1/2021.nlpmc-1.2

Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization

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Abstract

Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system.

Anthology ID:: 2021.nlpmc-1.2
Volume:: Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
Month:: June
Year:: 2021
Address:: Online
Editors:: Chaitanya Shivade, Rashmi Gangadharaiah, Spandana Gella, Sandeep Konam, Shaoqing Yuan, Yi Zhang, Parminder Bhatia, Byron Wallace
Venue:: NLPMC
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 10–20
Language:
URL:: https://aclanthology.org/2021.nlpmc-1.2/
DOI:: 10.18653/v1/2021.nlpmc-1.2
Bibkey:
Cite (ACL):: Wen-wai Yim and Meliha Yetisgen. 2021. Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization. In Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, pages 10–20, Online. Association for Computational Linguistics.
Cite (Informal):: Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization (Yim & Yetisgen, NLPMC 2021)
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PDF:: https://aclanthology.org/2021.nlpmc-1.2.pdf

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@inproceedings{yim-yetisgen-2021-towards,
    title = "Towards Automating Medical Scribing : Clinic Visit {D}ialogue2{N}ote Sentence Alignment and Snippet Summarization",
    author = "Yim, Wen-wai  and
      Yetisgen, Meliha",
    editor = "Shivade, Chaitanya  and
      Gangadharaiah, Rashmi  and
      Gella, Spandana  and
      Konam, Sandeep  and
      Yuan, Shaoqing  and
      Zhang, Yi  and
      Bhatia, Parminder  and
      Wallace, Byron",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.nlpmc-1.2/",
    doi = "10.18653/v1/2021.nlpmc-1.2",
    pages = "10--20",
    abstract = "Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system."
}

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%0 Conference Proceedings
%T Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization
%A Yim, Wen-wai
%A Yetisgen, Meliha
%Y Shivade, Chaitanya
%Y Gangadharaiah, Rashmi
%Y Gella, Spandana
%Y Konam, Sandeep
%Y Yuan, Shaoqing
%Y Zhang, Yi
%Y Bhatia, Parminder
%Y Wallace, Byron
%S Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F yim-yetisgen-2021-towards
%X Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system.
%R 10.18653/v1/2021.nlpmc-1.2
%U https://aclanthology.org/2021.nlpmc-1.2/
%U https://doi.org/10.18653/v1/2021.nlpmc-1.2
%P 10-20

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Markdown (Informal)

[Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization](https://aclanthology.org/2021.nlpmc-1.2/) (Yim & Yetisgen, NLPMC 2021)

Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization (Yim & Yetisgen, NLPMC 2021)

ACL

Wen-wai Yim and Meliha Yetisgen. 2021. Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization. In Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, pages 10–20, Online. Association for Computational Linguistics.