• 혜화의학회지
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• 제23권2호
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• pp.1-4
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• 2015

Learning can be defined as its objects, main question for the objects, and its unique way to organize all the knowledge acquired as the results of the question. From the point of view like this, Traditional Korean Medicine(TKM) can be defined as learning for human body and its functions, health and diseases based on the theory of the Yin and Yang and of the five elements. Nowaday Many papers based on laboratory work publish for the name of scientization of TKM, but from the viewpoint of definition of learning, they have a problem that there is no basic theory. If TKM could be communicated with western natural science, it has to be solved. And oriental physiology has a same object and same questions with western physiology, so oriental physiology can be useful to make a bridge between TKM and western natural science.

• 대한한의학회지
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• 제43권2호
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• pp.27-41
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• 2022

Objectives: There should be an objective analysis on the academic competency for incorporating Computer-based Test (CBT) in the education of traditional Korean medicine (TKM). However, the Item Response Theory (IRT) for analyzing latent competency has not been introduced for its difficulty in calculation, interpretation and utilization. Methods: The current study analyzed responses of 390 students of 8 years to the herbology test with 14 items by utilizing Rasch model, and the characteristics of test and items were evaluated by using characteristic curve, information curve, difficulty, academic competency, and test score. The academic competency of the students across gender and years were presented with scale characteristic curve, Kernel density map, and Wright map, and examined based on T-test and ANOVA. Results: The estimated item, test, and ability parameters based on Rasch model provided reliable information on academic competency, and organized insights on students, test and items not available with test score calculated by the summation of item scores. The test showed acceptable validity for analyzing academic competency, but some of items revealed difficulty parameters to be modified with Wright map. The gender difference was not distinctive, however the differences between test years were obvious with Kernel density map. Conclusion: The current study analyzed the responses in the herbology test for measuring academic competency in the education of TKM using Rasch model, and structured analysis for competency-based Teaching in the e-learning era was suggested. It would provide the foundation for the learning analytics essential for self-directed learning and competency adaptive learning in TKM.

• 정보관리연구
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• 제41권3호
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• pp.155-173
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• 2010

학문의 발전과 주제의 다양화로 인하여 각계의 연구자들은 자신에게 필요한 정보를 정확하게 찾을 필요성이 커지고 있다. 그리하여 본 논문에서는 효율적인 참고문헌 추출 방법으로 중복된 참고문헌을 비교 분석하여 자동으로 매핑해주는 시스템을 구축하고, 한의학 사전을 통한 한자의 오타를 교정할 수 있는 방법을 연구하였다. 이러한 방법을 적용함으로써 참고문헌의 중복입력과 한자오류를 개선할 수 있었다.

• Korean Journal of Acupuncture
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• 제33권3호
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• pp.102-113
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• 2016

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.

• 대한한의학회지
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• 제33권4호
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• pp.60-68
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• 2012

Objectives: By grasping trends in research, technology, and general characteristics of learning support tools, this study was conducted to present a model for research on Korean Medicine (KM) to make use of information technology to support teaching and learning. The purpose is to improve the future clinical competence of medical personnel, which is directly linked to national health. Methods: With papers and patents published up to 2011 as the objects, 438 papers were extracted from "Web of Science" and 313 patents were extracted from the WIPS database (DB). Descriptive analysis and network analysis were conducted on the annual developments, academic journals, and research fields of the papers, patents searched were subjected to quantitative analysis per application year, nation, and technology, and an activity index (AI) was calculated. Results: First, research on medical learning support tools has continued to increase and is active in the fields of computer engineering, education research, and surgery. Second, the largest number of patent applications on medical learning support tools were made in the United States, South Korea, and Japan in this order, and the securement of remediation technology-centered patents, rather than basic/essential patents, seemed possible. Third, when the results of the analysis of research trends were comprehensively analyzed, international research on e-PBL- and medical simulation-centered medical learning support tools was seen to expand continuously to improve the clinical competence of medical personnel, which is directly linked to national health. Conclusions: The KM learning support tool model proposed in the present study is expected to be applicable to computer-based tests at KM schools and to be able to replace certain functions of national KM doctor license examinations once its problem DB, e-PBL, and TKM simulator have been constructed. This learning support tool will undergo a standardization process in the future.

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.119-126
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• 2024

Diabetes is a chronic condition that happens when the pancreas fails to produce enough insulin or when the body's insulin is ineffectively used. Uncontrolled diabetes causes hyperglycaemia, or high blood sugar, which causes catastrophic damage to many of the body's systems, including the neurons and blood vessels, over time. The burden of disease on the global healthcare system is enormous. As a result, early diabetes diagnosis is critical in saving many lives. Current methods for determining whether a person has diabetes or is at risk of acquiring diabetes, on the other hand, rely heavily on clinical biomarkers. This research presents a unique deep learning architecture for predicting whether or not a person has diabetes and the severity levels of diabetes from the person's retinal image. This study incorporates datasets such as EyePACS and IDRID, which comprise Diabetic Retinopathy (DR) images and uses Dense-121 as the base due to its improved performance.