• Health Policy and Management
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• v.28 no.3
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• pp.294-300
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• 2018

The development and management of epidemiology intelligence service (EIS) officer with more specialized competence to cope with and prepare for health threats, including pandemic of emerging and re-emerging infectious diseases, is a high priority policy issue in Korea. First of all, we need to establish the training goal of EIS officer. It is necessary to establish manpower training and management system with at least three tiers including quantitative and qualitative targets. Second, at least 50% of all EIS officer must secure a physician and secure expertise and competence for epidemic. Third, for the ultimate purpose of EIS officer, the establishment of a public health expert should expand the scope of epidemiologist's work to health and medical care, occupational environment, and various disasters. Fourth, it is essential to expand the epidemiologist training and education program to the level of advanced countries. Especially, the training course should be expanded at least twice of current times. Fifth, it is necessary to independently install and operate the 'EIS Officer Training Center' as a mid- and long-term goal. Stewardship and governance are secured with the organization, personnel, etc. that can fully manage the planning, management, and evaluation of the EIS system. In the future, it will be necessary to establish a systematic and phased operational base of education and training programs for EIS officer, and establish a sustainable implementation system for strategy development. In addition, it is urgent to revise the guidelines for training public health professionals and strengthening competencies, and for establishing professional educational institutions.

• Annual Conference of KIPS
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• 2024.10a
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• pp.653-655
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• 2024

Lung cancer is a leading cause of death worldwide, highlighting the critical need for early diagnosis. Lung image analysis and segmentation are essential steps in this process, but manual segmentation of medical images is extremely time-consuming for radiation oncologists. The complexity of this task is heightened by the significant variability in lung tumors, which can differ greatly in size, shape, and texture due to factors like tumor subtype, stage, and patient-specific characteristics. Traditional segmentation methods often struggle to accurately capture this diversity. To address these challenges, we propose a lung cancer diagnosis system based on Mask2Former, utilizing CT (Computed Tomography) and PET (Positron Emission Tomography) images. This system excels in generating high-quality instance segmentation masks, enabling it to better adapt to the heterogeneous nature of lung tumors compared to traditional methods. Additionally, our system classifies the segmented output as either benign or malignant, leveraging a self-supervised network. The proposed approach offers a powerful tool for early diagnosis and effective management of lung cancer using CT and PET data. Extensive experiments demonstrate its effectiveness in achieving improved segmentation and classification results.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.329-337
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• 2007

Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and Rule Induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.947-971
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• 2022

The Electronic Medical Record (EMR) is a valuable source of medical data intelligence in e-health systems. The watermarking techniques have been used to authenticate the owner and protect the EMR from illegal copying. The existing distributive strategies, successfully operated to secure the EMR, are found to be inadequate. Blockchain technology, mainly, is employed by a sharing database that allows the digital crypto-currency. It rapidly leads to the magnified expectations acme. In this excitement, the use of consortium adopting the technology based on Blockchain, in the EMR structure, is found improving. This type of consortium adds an immutable share with a translucent record of the entire business and it is accomplished with responsibility, along with faith and transparency. The combination of watermarking and Blockchain technology provides a singular chance to promote a secured, trustworthy electronic documents administration to share with the e-records system. The authors, in this article, present their views on consortium Blockchain technology which is incorporated in the EMR system. The ledger, used for the distribution of the block structure, has team healthcare models based on dissimilar multiple image watermarking techniques.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

• Journal of Genetic Medicine
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• v.18 no.1
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• pp.16-23
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• 2021

GLB1-related disorders comprise two phenotypically unique disorders: GM1 gangliosidosis and Morquio B disease. These autosomal recessive disorders are caused by b-galactosidase deficiency. A hallmark of GM1 gangliosidosis is central nervous system degeneration where ganglioside synthesis is highest. The accumulation of keratan sulfate is the suspected cause of the bone findings in Morquio B disease. GM1 gangliosidosis is clinically characterized by a neurodegenerative disorder associated with dysostosis multiplex, while Morquio B disease is characterized by severe skeletal manifestations and the preservation of intelligence. Morquio B disease and GM1 gangliosidosis may be on a continuum of skeletal involvement. There is currently no effective treatment for GLB1-related disorders. Recently, multiple interventions have been developed and there are several ongoing clinical trials.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.146.2-146
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• 2001

Recently, a need of non-industrial robot, such as service, medical, entertainment and house-keeping robot, has been increased. Therefore, the capability of robot which can do intelligent behavior like interaction with men and its environment become more prominent than the capability of executing simple repetitive task. To implement an intelligent robot which provides intelligent behavior, an effective system architecture including perception, learning, reasoning and action part is necessary. Control architectures for intelligent robot can be divided into two different classes. One is deliberative type controller which is applicate to high level intelligence like environment ...

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.685-687
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• 2001

본 논문은 IIS 4.0 웹서버상에서는 ASP와 SQL을 연동한 웹프로그래밍을 통하여 효율적인 자료처리와 환자와 의사간의 on-line 상담, 그리고 off-line 상에서의 진료와 환자가 지정한 약사로의 처방전 전송 및 조제, 그리고 진료데이터의 저장 및 검색으로 인한 반영구적인 진료데이터저장 등 3자(환자, 의사, 약사)간의 상호대화형 원격진료 시스템구현에 대한 내용이다. 또한 본 시스템은 인터넷 기반에서의 3차(환자, 의사, 약사)간의 효율적인 진료와 빠른 처리를 위한 전자진료 차트 및 자료처리에 관한 내용을 제시하고 있다.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.