Clinical Concept Extraction for Document-Level Coding

Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, Jacob Eisenstein


Abstract
The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been information extraction: linking spans of text to concepts in a detailed domain ontology. However, recent work has demonstrated the potential of supervised machine learning to extract document-level codes directly from the raw text of clinical notes. We propose to bridge the gap between the two approaches with two novel syntheses: (1) treating extracted concepts as features, which are used to supplement or replace the text of the note; (2) treating extracted concepts as labels, which are used to learn a better representation of the text. Unfortunately, the resulting concepts do not yield performance gains on the document-level clinical coding task. We explore possible explanations and future research directions.
Anthology ID:
W19-5028
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
261–272
Language:
URL:
https://aclanthology.org/W19-5028
DOI:
10.18653/v1/W19-5028
Bibkey:
Cite (ACL):
Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, and Jacob Eisenstein. 2019. Clinical Concept Extraction for Document-Level Coding. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 261–272, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Clinical Concept Extraction for Document-Level Coding (Wiegreffe et al., BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5028.pdf