@inproceedings{sui-etal-2023-storyline,
title = "Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts",
author = "Sui, Peiqi and
Wong, Kelvin and
Yu, Xiaohui and
Volpi, John and
Wong, Stephen",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.45",
doi = "10.18653/v1/2023.clinicalnlp-1.45",
pages = "422--432",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts
%A Sui, Peiqi
%A Wong, Kelvin
%A Yu, Xiaohui
%A Volpi, John
%A Wong, Stephen
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sui-etal-2023-storyline
%X 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.
%R 10.18653/v1/2023.clinicalnlp-1.45
%U https://aclanthology.org/2023.clinicalnlp-1.45
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.45
%P 422-432
Markdown (Informal)
[Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts](https://aclanthology.org/2023.clinicalnlp-1.45) (Sui et al., ClinicalNLP 2023)
ACL