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http://dx.doi.org/10.3745/KTSDE.2022.11.5.203

Deep Learning-Based Model for Classification of Medical Record Types in EEG Report  

Oh, Kyoungsu (가천대학교 컴퓨터공학과)
Kang, Min (가천대학교 IT융합공학과)
Kang, Seok-hwan (가천대학교 컴퓨터공학과)
Lee, Young-ho (가천대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.5, 2022 , pp. 203-210 More about this Journal
Abstract
As more and more research and companies use health care data, efforts are being made to vitalize health care data worldwide. However, the system and format used by each institution is different. Therefore, this research established a basic model to classify text data onto multiple institutions according to the type of the future by establishing a basic model to classify the types of medical records of the EEG Report. For EEG Report classification, four deep learning-based algorithms were compared. As a result of the experiment, the ANN model trained by vectorizing with One-Hot Encoding showed the highest performance with an accuracy of 71%.
Keywords
Deep Learning; EEG Report Classification; Natural Language Processing;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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