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

3rd Clinical Natural Language Processing Workshop (Clinical NLP 2020) Workshop Paper

You can open the pre-recorded video in a separate window.

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.
NOTE: Video may display a random order of authors. Correct author list is at the top of this page.