• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.5
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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.

• Quality Improvement in Health Care
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• v.19 no.1
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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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• v.53 no.1
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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.

• Journal of Information Technology Services
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• v.10 no.1
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-6
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• 2009

One of the most important problems in bioinformatics is how to extract the useful information from a huge amount of data, and make a decision in diagnosis, prognosis, and medical treatment applications. This paper proposes a weighted kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a learning method based on genetic algorithm. The weights of basis kernel functions in proposed kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions.

• Health Policy and Management
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• v.24 no.4
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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.

• Journal of Korean Academy of Nursing
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• v.49 no.5
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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.

• Information Systems Review
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• v.15 no.2
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• pp.59-73
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• 2013

Healthcare costs are continuously increasing due to longer life expectancy and providing global healthcare services through medical tourism is new service growth engine for Korea. Several countries have well established programs and infrastructure dedicated to medical tourism. South Korea is attempting to become a major player in this domain by undertaking broad initiatives. The success of medical tourism is greatly impacted by easy access to two types of information, namely, medical and travel information. The National Health Insurance System in Korea collects huge amount of clinical and financial information from all hospitals. However, this information does not get used effectively in health and travel information systems to support medical tourism. This paper provide clear process map of medical tourism to understand how the patient and information process both medical and tourism fields also describe the need of customer and service provider. In this paper, we develop a medical tourism service system that will promote information exchange and service delivery.

• Quality Improvement in Health Care
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• v.14 no.1
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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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.