COLING 2022main5 citations
A New Public Corpus for Clinical Section Identification: MedSecId
Paul Landes, Kunal Patel, Sean S. Huang, Adam Webb, Barbara Di Eugenio, Cornelia Caragea
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.
BibTeX
@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"
}