Korean Medicine Symptom Recommendation Based on Textual Description of the Patient’s Condition. (Digestive disorder version) (자연어 기술 기반 한의 증상명 추천 시스템 (소화기계 질환 버전))

Jang Ho (장호); Lee Sanghun (이상훈); Yea Sang-Jun (예상준)

doi:10.34163/jkits.2020.15.5.025

Korean Medicine Symptom Recommendation Based on Textual Description of the Patient’s Condition. (Digestive disorder version)

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2020, 15(5), pp.831-843
DOI : 10.34163/jkits.2020.15.5.025
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : September 16, 2020
Accepted : October 13, 2020
Published : October 31, 2020

Jang Ho ¹, Lee Sanghun ¹, Yea Sang-Jun ¹

¹한국한의학연구원

Accredited

ABSTRACT

We developed a suggestion system that recommends related Korean Medicine symptoms from natural language description of patient's health condition. First, we collected medical information from 200 patients who suffer digestive disorder. Then, three groups of Korean medicine doctors belonging to each of the major Korean medicine associations examined clinical report forms (CRFs) of the patients and specified primary symptoms of the patients. The Korean medicine doctors also attached text evidences of their decision based on the descriptions in the CRFs. Those text descriptions and the symptoms were used as the input text and the label of the system. The features of the multi label support vector machine are term frequencies of the input text. Those terms are the morphemes from Korean morphological analyzer. The model performance measure was the area under cover (AUC) values that is calculated based on the predicted ranks of the true symptom. All AUC values of the models from three independent datasets generated by the three different groups of Korean medicine doctors are more than 0.85 which suggests our system shows consistent and robust performances regardless of the given datasets. Our study is an artificial intelligence (AI) application to Korean medicine and our approach is useful to explore proper symptom names from the thousands of Korean medicine terms. It can be expanded to application to other kinds of disorders easily. Better performance can be achieved by adapting advanced text processing technique and AI models.

KEYWORDS

CDSS, Text mining, Text classification, Korean medicine, Symptom recommendation

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* References for papers published after 2023 are currently being built.

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Korean Medicine Symptom Recommendation Based on Textual Description of the Patient’s Condition. (Digestive disorder version)

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Citation status

* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Korean Medicine Symptom Recommendation Based on Textual Description of the Patient’s Condition. (Digestive disorder version)

ABSTRACT

KEYWORDS

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