Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts
Peiqi Sui, Kelvin Wong, Xiaohui Yu, John Volpi, Stephen Wong
Abstract
Aphasia and dysarthria are both common symptoms of stroke, affecting around 30% and 50% of acute ischemic stroke patients. In this paper, we propose a storyline-centric approach to detect aphasia and dysarthria in acute stroke patients using transcribed picture descriptions alone. Our pipeline enriches the training set with healthy data to address the lack of acute stroke patient data and utilizes knowledge distillation to significantly improve upon a document classification baseline, achieving an AUC of 0.814 (aphasia) and 0.764 (dysarthria) on a patient-only validation set.- Anthology ID:
- 2023.clinicalnlp-1.45
- Volume:
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Anna Rumshisky
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 422–432
- Language:
- URL:
- https://aclanthology.org/2023.clinicalnlp-1.45
- DOI:
- 10.18653/v1/2023.clinicalnlp-1.45
- Cite (ACL):
- Peiqi Sui, Kelvin Wong, Xiaohui Yu, John Volpi, and Stephen Wong. 2023. Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 422–432, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts (Sui et al., ClinicalNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.clinicalnlp-1.45.pdf