Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records

Tak-Sung Heo, Chulho Kim, Jeong-Myeong Choi, Yeong-Seok Jeong, Yu-Seop Kim


Abstract
Stroke is one of the leading causes of death and disability worldwide. Stroke is treatable, but it is prone to disability after treatment and must be prevented. To grasp the degree of disability caused by stroke, we use magnetic resonance imaging text records to predict stroke and measure the performance according to the document-level and sentence-level representation. As a result of the experiment, the document-level representation shows better performance.
Anthology ID:
2020.clinicalnlp-1.1
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2020.clinicalnlp-1.1
DOI:
10.18653/v1/2020.clinicalnlp-1.1
Bibkey:
Cite (ACL):
Tak-Sung Heo, Chulho Kim, Jeong-Myeong Choi, Yeong-Seok Jeong, and Yu-Seop Kim. 2020. Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 1–6, Online. Association for Computational Linguistics.
Cite (Informal):
Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records (Heo et al., ClinicalNLP 2020)
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PDF:
https://aclanthology.org/2020.clinicalnlp-1.1.pdf
Video:
 https://slideslive.com/38939817