대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제43권2호
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  • Pages.109-115
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  • 2022
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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의무 기록 문서 분류를 위한 자연어 처리에서 최적의 벡터화 방법에 대한 비교 분석

Comparative Analysis of Vectorization Techniques in Electronic Medical Records Classification

  • 유성림 (성균관대학교 SAIHST 의료기기산업학과)
  • Yoo, Sung Lim (Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University)
  • 투고 : 2022.02.22
  • 심사 : 2022.04.15
  • 발행 : 2022.04.30
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초록

Purpose: Medical records classification using vectorization techniques plays an important role in natural language processing. The purpose of this study was to investigate proper vectorization techniques for electronic medical records classification. Material and methods: 403 electronic medical documents were extracted retrospectively and classified using the cosine similarity calculated by Scikit-learn (Python module for machine learning) in Jupyter Notebook. Vectors for medical documents were produced by three different vectorization techniques (TF-IDF, latent sematic analysis and Word2Vec) and the classification precisions for three vectorization techniques were evaluated. The Kruskal-Wallis test was used to determine if there was a significant difference among three vectorization techniques. Results: 403 medical documents were relevant to 41 different diseases and the average number of documents per diagnosis was 9.83 (standard deviation=3.46). The classification precisions for three vectorization techniques were 0.78 (TF-IDF), 0.87 (LSA) and 0.79 (Word2Vec). There was a statistically significant difference among three vectorization techniques. Conclusions: The results suggest that removing irrelevant information (LSA) is more efficient vectorization technique than modifying weights of vectorization models (TF-IDF, Word2Vec) for medical documents classification.

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참고문헌

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