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


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 5, pp. 203-210, May. 2022
https://doi.org/10.3745/KTSDE.2022.11.5.203,   PDF Download:
Keywords: Deep Learning, EEG Report Classification, Natural Language Processing
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%.


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Cite this article
[IEEE Style]
K. Oh, M. Kang, S. Kang, Y. Lee, "Deep Learning-Based Model for Classification of Medical Record Types in EEG Report," KIPS Transactions on Software and Data Engineering, vol. 11, no. 5, pp. 203-210, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.5.203.

[ACM Style]
Kyoungsu Oh, Min Kang, Seok-hwan Kang, and Young-ho Lee. 2022. Deep Learning-Based Model for Classification of Medical Record Types in EEG Report. KIPS Transactions on Software and Data Engineering, 11, 5, (2022), 203-210. DOI: https://doi.org/10.3745/KTSDE.2022.11.5.203.