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JKM > Volume 41(3); 2020 > Article
Lee, Jang, and Sung: Donguibogam-Based Pattern Diagnosis Using Natural Language Processing and Machine Learning

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.

Fig. 1
Natural Language Processing and Pattern Diagnois Prediction Model Based on Donguibogam.
jkm-41-3-1f1.gif
Fig. 2
Optimization of Word2Vector Model.
jkm-41-3-1f2.gif
Fig. 3
Word2Vector Model-Based Diagnostic Prediction Program.
jkm-41-3-1f3.gif

참고문헌

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