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
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-5028.pdf