Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes

Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt, James Gilkerson


Abstract
Identifying the reasons for antibiotic administration in veterinary records is a critical component of understanding antimicrobial usage patterns. This informs antimicrobial stewardship programs designed to fight antimicrobial resistance, a major health crisis affecting both humans and animals in which veterinarians have an important role to play. We propose a document classification approach to determine the reason for administration of a given drug, with particular focus on domain adaptation from one drug to another, and instance selection to minimize annotation effort.
Anthology ID:
2020.bionlp-1.17
Volume:
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
Month:
July
Year:
2020
Address:
Online
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
156–166
Language:
URL:
https://aclanthology.org/2020.bionlp-1.17
DOI:
10.18653/v1/2020.bionlp-1.17
Bibkey:
Cite (ACL):
Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt, and James Gilkerson. 2020. Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 156–166, Online. Association for Computational Linguistics.
Cite (Informal):
Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes (Hur et al., BioNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2020.bionlp-1.17.pdf
Video:
 http://slideslive.com/38929650