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자연어 처리 및 기계학습을 통한 동의보감 기반 한의변증진단기술 개발

Donguibogam-Based Pattern Diagnosis Using Natural Language Processing and Machine Learning

대한한의학회지 2020년 41권 3호 p.1 ~ 8
이승현, 장동표, 성강경,
소속 상세정보
이승현 ( Lee Seung-Hyeon ) - Hanyang University Department of Information System
장동표 ( Jang Dong-Pyo ) - Hanyang University Department of Biomedical Engineering
성강경 ( Sung Kang-Kyung ) - Wonkwang University College of Oriental Medicine Department of Internal Medicine

Abstract


Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning.

Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom’s vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression.

Results: The Word2Vec model’s maximum performance was obtained by optimizing Word2Vec’s primary parameters?the number of iterations, the vector’s dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis.

Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

키워드

Word2vector; Differentiation and Pattern Identification of Symptoms; Word Embedding; Natural Language Processing; Donguibogam

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