Abstract
Prior studies have raised concerns over specificity issues in clinical advice. Lacking specificity — explicitly discussed detailed information — may affect the quality and implementation of clinical advice in medical practice. In this study, we developed and validated a fine-grained annotation schema to describe different aspects of specificity in clinical advice extracted from medical research literature. We also presented our initial annotation effort and discussed future directions towards an NLP-based specificity analysis tool for summarizing and verifying the details in clinical advice.- Anthology ID:
- 2022.unimplicit-1.3
- Volume:
- Proceedings of the Second Workshop on Understanding Implicit and Underspecified Language
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, USA
- Editors:
- Valentina Pyatkin, Daniel Fried, Talita Anthonio
- Venue:
- unimplicit
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17–21
- Language:
- URL:
- https://aclanthology.org/2022.unimplicit-1.3
- DOI:
- 10.18653/v1/2022.unimplicit-1.3
- Cite (ACL):
- Yingya Li and Bei Yu. 2022. “Devils Are in the Details”: Annotating Specificity of Clinical Advice from Medical Literature. In Proceedings of the Second Workshop on Understanding Implicit and Underspecified Language, pages 17–21, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- “Devils Are in the Details”: Annotating Specificity of Clinical Advice from Medical Literature (Li & Yu, unimplicit 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.unimplicit-1.3.pdf