• 동의생리병리학회지
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• 제35권5호
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• pp.132-138
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• 2021

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

• 한국의료질향상학회지
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• 제19권1호
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• pp.16-26
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• 2013

Health information technology (HIT) is one of the most familiar tools to healthcare providers. It is used in routine practice to reduce cost, to improve clinical performance, and to improve patient safety. Patient safety is the driving force of recent expansion of HIT industry. But there are many evidences that it can be harmful to patient safety. Role of HIT and HIT-related error became big issues because more and more healthcare providers and healthcare organizations are willing to adopt it. Adoption rate of HIT in Korea is higher than that of United States. But researches of HIT regarding patient safety are rare. In this article, types of HIT, their mechanisms of improving patient safety and HIT-related errors were reviewed. Status of HIT in terms of patient safety in Korea was also reviewed. Knowledge of how HIT can improve patient safety, its' limitation, and how to make it safer is crucial to whom have to use it to improve patient safety. Impact of HIT on patient safety must be evaluated actively in Korea. HIT which was proven to improve patient safety must be widely adopted. Government must prepare a strategic plan to improve HIT quality, support hospitals financially and institutionally to introduce qualified HIT, and develop HIT infrastructures and standard designed for patient safety.

• Journal of Periodontal and Implant Science
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• 제53권1호
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

• 한국IT서비스학회지
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• 제10권1호
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• pp.135-149
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• 2011

In this paper, we propose the high-risk group detection model for patients with high blood pressure using case-based reasoning. The proposed model can be applied for public health maintenance organizations to effectively manage knowledge related to high blood pressure and efficiently allocate limited health care resources. Especially, the focus is on the development of the model that can handle constraints such as managing large volume of data, enabling the automatic learning to adapt to external environmental changes and operating the system on a real-time basis. Using real data collected from local public health centers, the optimal high-risk group detection model was derived incorporating optimal parameter sets. The results of the performance test for the model using test data show that the prediction accuracy of the proposed model is two times better than the natural risk of high blood pressure.

• 한국인터넷방송통신학회논문지
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• 제9권2호
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• pp.1-6
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• 2009

많은 양의 데이터로부터 유용성있는 정보의 추출, 진단 및 예후에 대한 결정, 질병 치료의 응용 등은 바이오 인포머틱스(Bioinformatics)분야에서 매우 중요한 문제들이다. 본 논문에서는 암진단시스템에 적용하기위해 support vector machine을 위한 weogjted lernel fuction과 빠른 수렴성과 좋은 분류성능을 갖는 학습방법을 제안하였다. 제안된 kernel function에서 기본적인 kernel fuction의 weights는 암진단 학습단계에서 결정되고 분류단계에서 파리미터로 사용된다. 대장암 데이터와 같은 임상 데이터에 대한 실험결과에서 제안된 방법은 기존의 다른 kernel fuction들 보다 더 우수하고 안정적인 분류성능을 보여주었다.

• 보건행정학회지
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• 제24권4호
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• pp.312-321
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• 2014

Background: To evaluate the quality of chronic care provided by public health centers located in a South Korean metropolitan city using a modified Assessment of Chronic Illness Care (ACIC). Methods: We conducted self-evaluation surveys and collected data using a modified ACIC from twenty five public health centers. Cultural validity of the original ACIC was examined by the public health and nursing science experts. Based on expert reviews, cognitive interviews, pre-test results, five items of the original ACIC that were not relevant were deleted. The response scale was changed from twelve-point Likert scale to Guttman scale but its scoring system was maintained. Results: Eighty eight percent of public health centers in this study reported that their overall quality of chronic care was at a limited or basic level. About 68% of the centers reported that the organization was as reasonably good or fully developed to provide chronic care. On the other hand, 96% of the public health centers reported that the clinical information system was at a very limited or basic support level. The decision support, the integration of Chronic Care Model components, the delivery system design, the community linkages, and the self-management support were evaluated as limited or basic level of support by more than half of the public health centers, respectively. Conclusion: In a metropolitan area of South Korea, quality of chronic care in public health centers was not found to reach acceptable levels of services. It is critical to enhance the quality of chronic care in public health centers.

• 대한간호학회지
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• 제49권5호
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• pp.575-585
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• 2019

Purpose: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. Methods: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. Results: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. Conclusion: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

• 경영정보학연구
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• 제15권2호
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• pp.59-73
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• 2013

인간의 수명연장에 따라 의료비는 꾸준히 상승하고 있고 이에 따라 의료관광시장도 급 성정하고 있다. 한국은 우수한 의료진과 의료인프라를 기반으로 하여 세계의료관광시장에서 두각을 나타내고 있으며 정부에서도 전략성장산업으로 육성, 지원하고 있다. 의료관광산업의 성패는 '의료정보'와 '관광정보'의 접근용이성에 크게 영향을 받을 수밖에 없다. 건강보험공단 등이 많은 의료관련정보를 보유하고 있고 관광관련기관에서도 많은 관광관련정보를 보유하고 있지만 이들 정보가 의료관광객에게 효과적으로 제공되지는 못하고 있는 실정이다. 여기서는 의료관광에 있어 환자와 정보가 의료분야와 관광 분야에서 전달되는지 에 대한 명확한 도표와 서비스 수요자와 공급자의 필요에 대한 것들을 설명하고 있다. 본 논문에서는 의료관광의 소비자와 공급자간에 원활한 정보소통과 효과적 서비스전달을 위한 의료관광 서비스시스템 디자인을 제안하고자 한다.

• 한국의료질향상학회지
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• 제14권1호
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• pp.49-54
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• 2008

Background : This study was to reduce incidence of falls by analyzing actual problem and drawing out improvement plan applicable to the clinical practice through operation of the staff nurses-centered fall peer review group. Method : The fall peer review group was composed of 8 nurses having patient nursing experience for over 5 years, and each of fall cases was reviewed and the root cause was analyzed. As a result, it was found that the patients and their families did not fully understandthe content of the education, and the staff nurses did not completely inspect the risk factors of falls and perform immediate intervention when patient's condition changed. Based on the above-mentioned results, improvement activity was conducted for the purposes of consolidating patients education method and supplementing computerized system to support nurses' decision making as well as devices and facilities. Result : As a result of conducting improvement activity in the aspects of education for patients, support of nurse's decision-making, and devices and facilities through operation of the staff nurses-centered fall peer review group, falls decreased by 9.5% compared to before improvement activity. Conclusion : It is concluded that operation of the clinical nurses-centered fall peer review group played a role of promoter to draw out practical and applicable improvement plan to the clinical practice and apply directions of the field-centered, and increased nurses' interest in falls and ultimately, reduced incidence of falls. Therefore the Center will continue to operate the staff nurses-centered peer review group, and recommends participation of nurses who actually take the charge of nursing patients in further analysis of patients' safety accidents.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.617-620
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• 2007

최근 건강에 대한 관심이 높아지면서 의료 분야를 지원하는 애플리케이션 개발이 활발히 이루어지고 있다. 심장의 상태를 곡선으로 나타내는 ECG 를 기반으로 심장병의 유무를 발견하는 애플리케이션은 의료 분야 애플리케이션의 좋은 예라고 할 수 있다. ECG 만으로 질환을 판단하는 것은 제약이 있어, 이를 극복하기 위해 MCG 혹은 가상심장과 같은 다른 자원을 활용하는 것은 좋은 방법이다. 이와 같은 통합 시스템을 지원하려면 각 도메인에 대한 지식이 정의되어야 한다. 이에 본 연구에서는 ECG 와 심장계 질환에 대한 지식을 온톨로지를 이용하여 구축하고 ECG 를 통해 질환을 발견할 수 있는 추론 시스템을 제안하고 프로토타입 시스템을 개발한다.