Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Conceptualisation and annotation of drug nonadherence Information for knowledge extraction from patient-generated texts

Belz, Anya orcid logoORCID: 0000-0002-0552-8096, Hoile, Richard orcid logoORCID: 0000-0001-5003-1655, Ford, Elizabeth orcid logoORCID: 0000-0001-5613-8509 and Mullick, Azam (2019) Conceptualisation and annotation of drug nonadherence Information for knowledge extraction from patient-generated texts. In: 5th Workshop on Noisy User-generated Text (W-NUT 2019), 4 Nov 2019, Hong Kong, China.

Abstract
Approaches to knowledge extraction (KE) in the health domain often start by annotating text to indicate the knowledge to be extracted, and then use the annotated text to train systems to perform the KE. This may work for annotating named entities or other contiguous noun phrases (drugs, some drug effects), but be- comes increasingly difficult when items tend to be expressed across multiple, possibly non- contiguous, syntactic constituents (e.g. most descriptions of drug effects in user-generated text). Other issues include that it is not al- ways clear how annotations map to actionable insights, or how they scale up to, or can form part of, more complex KE tasks. This paper reports our efforts in developing an approach to extracting knowledge about drug nonadherence from health forums which led us to conclude that development cannot proceed in separate steps but that all aspects—from conceptualisation to annotation scheme development, annotation, KE system training and knowledge graph instantiation—are interdependent and need to be co-developed. Our aim in this paper is two-fold: we describe a generally applicable framework for developing a KE approach, and present a specific KE approach, developed with the framework, for the task of gathering information about antidepressant drug nonadherence. We report the conceptualisation, the annotation scheme, the annotated corpus, and an analysis of annotated texts.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Xu, Wei, Baldwin, Tim and Rahimi, Afshin, (eds.) Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://doi.org/10.18653/v1/D19-5526
Copyright Information:© 2019 Association for Computational Linguistics
Funders:CoQuaND project funded by the EPSRC UK Healthcare Text Analytics Research Network (Healtex, EP/N027280).
ID Code:28626
Deposited On:07 Jul 2023 12:42 by Anya Belz . Last Modified 07 Jul 2023 12:42
Documents

Full text available as:

[thumbnail of D19-5526.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution 4.0
338kB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record