<i>Donguibogam</i>-Based Pattern Diagnosis Using Natural Language Processing and Machine Learning

Seung Hyeon Lee; Dong Pyo Jang; Kang Kyung Sung; Lee, Seung Hyeon; Jang, Dong Pyo; Sung, Kang Kyung

doi:10.13048/jkm.20021

JKM > Volume 41(3); 2020 > Article

Lee, Jang, and Sung: Donguibogam-Based Pattern Diagnosis Using Natural Language Processing and Machine Learning

Original Article

The Journal of Korean Medicine 2020; 41(3): 1-8.

Published online: September 01, 2020

DOI: https://doi.org/10.13048/jkm.20021

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

Seung Hyeon Lee¹

, Dong Pyo Jang²

, Kang Kyung Sung³^{, *}

¹Department of Information System, Hanyang University, Seoul, Korea

²Department of Biomedical Engineering, Hanyang University, Seoul, Korea

³Department of Internal Medicine, College of Oriental Medicine, Wonkwang University, Iksan, Korea

Correspondence to: Kang Kyung Sung, Department of Internal Medicine, College of Oriental Medicine, Wonkwang University, 460 Iksan-daero, Sin-dong, Iksan 54538, Korea, Tel: +82-62-670-6412, Fax: +82-62-671-6414, E-mail: sungkk@wonkwang.ac.kr

Received June 30, 2020 Revised July 25, 2020 Accepted July 27, 2020

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.

Keywords: Word2vector, Differentiation and Pattern Identification of Symptoms, Word Embedding, Natural Language Processing, Donguibogam

Fig. 1

Natural Language Processing and Pattern Diagnois Prediction Model Based on Donguibogam.

Fig. 2

Optimization of Word2Vector Model.

Fig. 3

Word2Vector Model-Based Diagnostic Prediction Program.

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