A New Public Corpus for Clinical Section Identification: MedSecId
Paul Landes, Kunal Patel, Sean S. Huang, Adam Webb, Barbara Di Eugenio, Cornelia Caragea
Correct Metadata for
Abstract
The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation. The primary contribution of this work is MedSecId, a publicly available set of 2,002 fully annotated medical notes from the MIMIC-III. We include several baselines, source code, a pretrained model and analysis of the data showing a relationship between medical concepts across sections using principal component analysis.- Anthology ID:
- 2022.coling-1.326
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3709–3721
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.326/
- DOI:
- Bibkey:
- Cite (ACL):
- Paul Landes, Kunal Patel, Sean S. Huang, Adam Webb, Barbara Di Eugenio, and Cornelia Caragea. 2022. A New Public Corpus for Clinical Section Identification: MedSecId. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3709–3721, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- A New Public Corpus for Clinical Section Identification: MedSecId (Landes et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.326.pdf
- Code
- uic-nlp-lab/medsecid
- Data
- MedSecId, MIMIC-III
Export citation
@inproceedings{landes-etal-2022-new,
title = "A New Public Corpus for Clinical Section Identification: {M}ed{S}ec{I}d",
author = "Landes, Paul and
Patel, Kunal and
Huang, Sean S. and
Webb, Adam and
Di Eugenio, Barbara and
Caragea, Cornelia",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.326/",
pages = "3709--3721",
abstract = "The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation. The primary contribution of this work is MedSecId, a publicly available set of 2,002 fully annotated medical notes from the MIMIC-III. We include several baselines, source code, a pretrained model and analysis of the data showing a relationship between medical concepts across sections using principal component analysis."
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%0 Conference Proceedings %T A New Public Corpus for Clinical Section Identification: MedSecId %A Landes, Paul %A Patel, Kunal %A Huang, Sean S. %A Webb, Adam %A Di Eugenio, Barbara %A Caragea, Cornelia %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F landes-etal-2022-new %X The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation. The primary contribution of this work is MedSecId, a publicly available set of 2,002 fully annotated medical notes from the MIMIC-III. We include several baselines, source code, a pretrained model and analysis of the data showing a relationship between medical concepts across sections using principal component analysis. %U https://aclanthology.org/2022.coling-1.326/ %P 3709-3721
Markdown (Informal)
[A New Public Corpus for Clinical Section Identification: MedSecId](https://aclanthology.org/2022.coling-1.326/) (Landes et al., COLING 2022)
- A New Public Corpus for Clinical Section Identification: MedSecId (Landes et al., COLING 2022)
ACL
- Paul Landes, Kunal Patel, Sean S. Huang, Adam Webb, Barbara Di Eugenio, and Cornelia Caragea. 2022. A New Public Corpus for Clinical Section Identification: MedSecId. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3709–3721, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.