KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Deep Learning-Based Model for Classification of Medical Record Types in EEG Report

EEG Report의 의무기록 유형 분류를 위한 딥러닝 기반 모델

  • 오경수 (가천대학교 컴퓨터공학과) ;
  • 강민 (가천대학교 IT융합공학과) ;
  • 강석환 (가천대학교 컴퓨터공학과) ;
  • 이영호 (가천대학교 컴퓨터공학과)
  • Received : 2021.09.09
  • Accepted : 2021.10.12
  • Published : 2022.05.31
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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%.

보건의료 데이터를 사용하는 연구 및 기업이 늘어나며 세계적으로 보건의료 데이터 활성화를 위한 노력을 진행 중이다. 하지만 기관에 따라 사용하는 시스템과 서식이 다르다. 이에 본 연구는 EEG Report의 의무기록 유형을 분류하는 기저 모델 구축을 통해 향후 다기관의 텍스트 데이터를 유형에 따라 분류하는 기저 모델을 구축하였다. EEG Report 분류를 위해 4가지의 딥러닝 기반 알고리즘에 대해 비교하였다. 실험 결과 One-Hot Encoding으로 벡터화하여 학습한 ANN 모델이 71%의 정확도로 가장 높은 성능을 보였다.

Keywords

Acknowledgement

이 논문은 과학기술정보통신부 및 정보통신기획평가원의 대학 ICT센터육성지원사업의 지원으로 연구를 수행하였음(IITP-2021-2017-0-016630).

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