회원학회
회원학회
| 번호 | 연구제목 | 연구자 | 연구기간 | 발표실적 |
|---|---|---|---|---|
| 내용 | ||||
| 216 | 대청룡탕으로 호전된 말초신경병증 증례 2례 고찰 | 박재경 | 2023-03-19 ~ | 학술대회 |
| 대청룡탕으로 호전된 말초신경병증 증례 2례 고찰 | ||||
| 215 | 『傷寒論』 ‘發作’과 뇌전증의 연관성 : 다수증례보고 | 이성준, 김민환, 윤용갑, 임규상, 이숭인 | 2022-12-31 ~ | 학회지 |
| Objectives: We aimed to confirm the safety and effectiveness of the decoction of Sibjo-tang, which is a powder type purgative. In addition, we checked whether the '發作' of 『Shanghanlun』 can be interpreted to have the same meaning as “seizure” in nglish. By confirming these objectives, we intend to lead the progress in the application of Sibjo-tang and to expand the clinical application of the 152nd provision and Sibjo-tang. Methods: We analyzed the medical records of patients who visited Apgujeongjeongin Oriental Clinic and Almyeon Oriental Clinic for seizures. We confirmed side effects in |
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| 214 | 『傷寒論』 辨病診斷體系에 근거하여 茯?桂枝白朮甘草湯 투여 후 호전된 부정맥 증례 1례 | 이욱제 | 2022-12-31 ~ | 학회지 |
| Objective : This study aimed to report the improvement of one patient with arrhythmia treated using Bokryeonggyejibaekchulgamcho-tang based on the disease pattern identification diagnostic system (DPIDS) by Shanhanlun Provisions. Methods : The patient with arrhythmia was treated using Bokryeonggyejibaekchulgamcho-tang according to to Shanghanlun provisions. The results were evaluated using Modified European Heart Rhythm Association symptom scale (mEHRA). Results : After taking Bokryeonggyejibaekchulgamcho-tang for 257 days, mEHRA decreased from 3 to 1. Conclusions : The Administration of Bokryeonggyejibaekchulgamcho-tang to patient with arrhythmia, based on Shanhanlun DPIDS was effective. Key words : Bokryeonggyejibaekchulgamcho-tang, Ryokeijutsukanto, Linggui Zhugan Decoction, Yeonggyechulgam-tang, Arrhythmia, disease pattern identification diagnostic system by Shanghanlun provisions (DPIDS), Shanghanlun |
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| 213 | 자연어 처리 기반 『傷寒論』 辨病診斷體系 분류를 위한 기계학습 모델 선정 | 김영남 | 2022-12-31 ~ | 학회지 |
| Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, ‘Taeyangbyeong-gyeolhyung’ and ‘Eumyangyeokchahunobokbyeong’ were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an ccuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification. Key words : Artificial intelligence, Machine learning, Natural Language Processing, Shanghanlun, Diagnostic system |
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