@inproceedings{wiegreffe-etal-2019-clinical,
title = "Clinical Concept Extraction for Document-Level Coding",
author = "Wiegreffe, Sarah and
Choi, Edward and
Yan, Sherry and
Sun, Jimeng and
Eisenstein, Jacob",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5028",
doi = "10.18653/v1/W19-5028",
pages = "261--272",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Clinical Concept Extraction for Document-Level Coding
%A Wiegreffe, Sarah
%A Choi, Edward
%A Yan, Sherry
%A Sun, Jimeng
%A Eisenstein, Jacob
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F wiegreffe-etal-2019-clinical
%X 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.
%R 10.18653/v1/W19-5028
%U https://aclanthology.org/W19-5028
%U https://doi.org/10.18653/v1/W19-5028
%P 261-272
Markdown (Informal)
[Clinical Concept Extraction for Document-Level Coding](https://aclanthology.org/W19-5028) (Wiegreffe et al., BioNLP 2019)
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.