의무 기록 문서 분류를 위한 자연어 처리에서 최적의 벡터화 방법에 대한 비교 분석

• Published in: Journal of biomedical engineering research Vol. 43; no. 2; pp. 109 - 115
• Main Authors: 유성림, Yoo, Sung Lim
• Format: Journal Article
• Language: Korean
• Published: 대한의용생체공학회 01.04.2022
• Subjects: 의공학; Medical records classification; Natural language processing; Vectorization techniques; Latent semantic analysis; Machine learning
• ISSN: 1229-0807; 2288-9396

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.