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.
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
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
MIMIC-III