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논문 기본 정보

자료유형
학술저널
저자정보
김현호 (경희대학교 한의과대학 진단생기능의학과학교실) 양승범 (원광보건대학교 의무부사관과) 강연석 (원광대학교 한의과대학 의사학교실) 박영배 (경희대학교 한의과대학 진단생기능의학과학교실) 김재효 (원광대학교 한의과대학 경혈학교실)
저널정보
경락경혈학회 Korean Journal of Acupuncture Korean journal of acupuncture 제33권 제3호
발행연도
2016.1
수록면
102 - 113 (12page)

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Objectives : This study is aimed at developing and discussing the prediction model of blood stasis pattern of traditional Korean medicine(TKM) using machine learning algorithms: multiple logistic regression and decision tree model. Methods : First, we reviewed the blood stasis(BS) questionnaires of Korean, Chinese, and Japanese version to make a integrated BS questionnaire of patient-reported outcomes. Through a human subject research, patients-reported BS symptoms data were acquired. Next, experts decisions of 5 Korean medicine doctor were also acquired, and supervised learning models were developed using multiple logistic regression and decision tree. Results : Integrated BS questionnaire with 24 items was developed. Multiple logistic regression models with accuracy of 0.92(male) and 0.95(female) validated by 10-folds cross-validation were constructed. By decision tree modeling methods, male model with 8 decision node and female model with 6 decision node were made. In the both models, symptoms of 'recent physical trauma', 'chest pain', 'numbness', and 'menstrual disorder(female only)' were considered as important factors. Conclusions : Because machine learning, especially supervised learning, can reveal and suggest important or essential factors among the very various symptoms making up a pattern identification, it can be a very useful tool in researching diagnostics of TKM. With a proper patient-reported outcomes or well-structured database, it can also be applied to a pre-screening solutions of healthcare system in Mibyoung stage.

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